• The korean journal of orthodontics
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• v.30 no.5 s.82
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• pp.657-668
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• 2000

The purpose of this study was to evaluate the amount and interrelationship of the soft tissue of nose and maxillary changes and to identify the nasal morphologic features that indicate susceptibility to nasal deflection in such a manner that they would be useful in presurgical prediction of nasal changes after maxillary advancement surgery in skeletal Class III malocclusion. The sample consisted of 25 adult patients (13 males and 12 females) who had severe anteroposterior skeletal discrepancy. The patients had received presurgical orthodontic treatment. They underwent a Le Fort I advancement osteotomy, rigid internal fixation, alar cinch suture and V-Y advancement lip closure. The presurgical and postsurgical lateral cephalograms and lateral and frontal facial photographs were evaluated. The computerized statistical analysis was carried out. Soft tissue of nose change to h point change ratios were calculated by regression equations. The results were as follows 1. The correlation of maxillary hard tissue horizontal changes and nasal soft tissue vortical changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.228 at ANt, 0.257 at SNt. 2. The correlation of maxillary hard tissue and nasal soft tissue horizontal changes were high and the ${\beta}_0$ for soft tissue to ADV were 0.484 at ANt, 0.431 at SNt, 0.806 at Sn. 3. The correlation of maxillary hard tissue horizontal changes and width changes of ala of nose were high and the ${\beta}_0$ lot alar base width ratio to ADV were 0.002. 4. The DRI, Prominence of nose, Pre-Op CA is not a quantitative measure that can be used clinically to improve the predictability of vertical and horizontal nasal tip deflection. In this study, increases in nasal tip projection and anterosuperior rotation occur when there is an anterior vector of maxillary movement. These nasal changes were Quantitatively correlated to magnitude of maxillary(A point) movement.

• Korean Journal of Environment and Ecology
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• v.37 no.6
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• pp.429-438
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• 2023

The forest in the lower Tumen River serves as an important ecosystem spanning the territories of North Korea, Russia, and China, and it provides habitat and movement corridors for diverse mammals, including the endangered Amur tiger (Panthera tigris) and Amur leopard (Panthera pardus). This study focuses on the Mijiang area, situated as a potential ecological corridor connecting North Korea and China in the lower Tumen River, playing a crucial role in conserving and restoring the biodiversity of the Korean Peninsula. This study aimed to identify mammal species and estimate their relative abundance, occupancy, and distribution based on the 48 camera traps installed in the Mijiang area from May 2019 to May 2021. The results confirmed the presence of 18 mammal species in the Mijiang area, including large carnivores like tigers and leopards. Among the dominant mammals, four species of ungulates showed high occupancy and detection rates, particularly the Roe deer (Capreolus pygargus) and Wild boar (Sus scrofa). The roe deer was distributed across all areas with a predicted high occupancy rate of 0.97, influenced by altitude, urban residential areas, and patch density. Wild boars showed a predicted occupancy rate of 0.73 and were distributed throughout the entire area, with factors such as wetland ratio, grazing intensity, and spatial heterogeneity in aspects of the landscape influencing their occupancy and detection rates. Sika deer (Cervus nippon) exhibited a predicted occupancy rate of 0.48, confined to specific areas, influenced by slope, habitat fragmentation diversity affecting detection rates, and the ratio of open forests impacting occupancy. Water deer (Hydropotes inermis) displayed a very low occupancy rate of 0.06 along the Tumen River Basin, with higher occupancy in lower altitude areas and increased detection in locations with high spatial heterogeneity in aspects. This study confirmed that the Mijiang area serves as a habitat supporting diverse mammals in the lower Tumen River while also playing a crucial role in facilitating animal movement and habitat connectivity. Additionally, the occupancy prediction model developed in this study is expected to contribute to predicting mammal distribution within the disrupted Tumen River basin due to human interference and identifying and protecting potential ecological corridors in this transboundary region.

• Korean Journal of Cognitive Science
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• v.29 no.2
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• pp.121-135
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• 2018

Dancers' motions are perceived by observers through visual processes with visual information forming the basis for the observers' appreciation and evaluation of the dancers' motions. There have been many discussions as to whether or not observers' personal athletic capabilities form an essential basis for accurate assessment of the motions of others but, so far, no valid conclusions have been reached. The purpose of this study is to investigate how the ability to predict motions of others varies depending on the athletic expertise of the observers. Participants of this research were ballet dancers of varying athletic expertise. Twenty seven participants were divided into three groups with nine in each: beginners, intermediate experts and experts. The participants watched the same dance video and then evaluated whether the motion would be successful or not. The movement related visual information required to evaluate the success of the motion was systematically adjusted by controlling the length of the video. Using the temporal occlusion method, this study measured the response accuracy of the participants by category of expertise. Under the circumstance with insufficient visual information to utilize, the experts showed higher rates of correct response than the intermediate experts and the beginners. The beginners showed higher rates of wrong response than the experts and the intermediate experts. These results showed that the ability to predict success or failure of a dance motion varied depending on motion expertise of the observers, although they had similar level of expertise in perception. Participants considered to have high athletic expertise showed high prediction ability on the result of the motion. In addition, high expertise in perception reduced the likelihood that participants would make hasty responses under the circumstance with insufficient information and helped to reduce wrong response rate. In conclusion, this study showed that motor expertise and perceptual expertise contribute to prediction accuracy of observed motions.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.

• Journal of Animal Science and Technology
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• v.55 no.3
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• pp.165-171
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• 2013

Genomic relationship matrix (GRM) was constructed using whole genome SNP markers of swine and genomic breeding value was estimated by substitution of the numerator relationship matrix (NRM) based on pedigree information to GRM. Genotypes of 40,706 SNP markers from 448 pure Landrace pigs were used in this study and five kinds of GRM construction methods, G05, GMF, GOF, $GOF^*$ and GN, were compared with each other and with NRM. Coefficients of GOF considering each of observed allele frequencies showed the lowest deviation with coefficients of NRM and as coefficients of GMF considering the average minor allele frequency showed huge deviation from coefficients of NRM, movement of mean was expected by methods of allele frequency consideration. All GRM construction methods, except for $GOF^*$, showed normally distributed Mendelian sampling. As the result of breeding value (BV) estimation for days to 90 kg (D90KG) and average back-fat thickness (ABF) using NRM and GRM, correlation between BV of NRM and GRM was the highest by GOF and as genetic variance was overestimated by $GOF^*$, it was confirmed that scale of GRM is closely related with estimation of genetic variance. With the same amount of phenotype information, accuracy of BV based on genomic information was higher than BV based on pedigree information and these symptoms were more obvious for ABF then D90KG. Genetic evaluation of animal using relationship matrix by genomic information could be useful when there is lack of phenotype or relationship and prediction of BV for young animals without phenotype.

• The korean journal of orthodontics
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• v.32 no.5 s.94
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• pp.313-325
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• 2002

Three-dimensional CT imaging is efficient in examining specific structures in the craniofacial area by reproducing actual measurements through minimization of errors from patient movement and image magnification. Due to the rapid development of digital image technology and the expansion of treatment range a need for developing three -dimensional analysis has become urgent. Therefore the purpose of this study was to evaluate the percentage of error and magnification of three-dimensional CT using a dried skull and Vworks $program^{TM}$ (Cybermed Inc., Seoul, Korea) and also to obtain landmarks that are easy to designate and reproduce in three-dimensional images using the Vmorph-proto $program^{TM}$ (Cybermed Inc., Seoul, Korea). The following conclusions were obtained, 1. In the comparison of actual measurements from the dried skull and the three-dimensional image obtained from the Vworks program, the mean error was 0.99mm and the magnification was 1.04%. 2. Clinically useful hard tissue landmarks from three-dimensional images were Supraorbitale, Lateral orbital margin, Infraorbitale, Nasion, ANS, A point, Zygomaticomaxilla, Upper incisor, Lower incisor, B point, pogonion, Menton, PNS, Condylar inner margin, Condylar outer margin, Porion, Condylion, Gonionl, Gonion2, Gonion3, Sigmoid notch and Basion. 3. Clinically useful soft tissue landmarks from three-dimensional images were Endocanthion, Exocanthion, Soft tissue Nasion, Pronasale, Alare lateralis, Upper nostril point, Lower nostril point, Subnasale, Upper lip point, Cheilion, Stomion, Lower lip center, Soft tissue B, Pogonion, Menton and Preaurale. The Vworks program can be considered a clinically efficient tool to produce and measure three-dimensional images. Most of the hard and soft tissue landmarks proposed above are anatomically important points which are also easily reproducible and designated. These landmarks can be beneficial in three-dimensional diagnosis and the prediction of changes before and after surgery.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1773-1784
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• 2021

In order to minimize damage to oil spill accidents in the ocean, it is essential to collect a spilled area as soon as possible. Thus satellite-based remote sensing is a powerful source to detect oil spills in the ocean. With the recent rapid increase in the number of available satellites, it has become possible to generate a status report of marine oil spills soon after the accident. In this study, the oil spill area was calculated using various satellite images for the Symphony oil spill accident that occurred off the coast of Qingdao Port, China, on April 27, 2021. In particular, improving the accuracy of oil spill area determination was applied using high-resolution commercial satellite images with a spatial resolution of 2m. Sentinel-1, Sentinel-2, LANDSAT-8, GEO-KOMPSAT-2B (GOCI-II) and Skysat satellite images were collected from April 27 to May 13, but five images were available considering the weather conditions. The spilled oil had spread northeastward, bound for coastal region of China. This trend was confirmed in the Skysat image and also similar to the movement prediction of oil particles from the accident location. From this result, the look-alike patch observed in the north area from the Sentinel-1A (2021.05.01) image was discriminated as a false alarm. Through the survey period, the spilled oil area tends to increase linearly after the accident. This study showed that high-resolution optical satellites can be used to calculate more accurately the distribution area of spilled oil and contribute to establishing efficient response strategies for oil spill accidents.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.