KSII Transactions on Internet and Information Systems (TIIS)
/
v.18
no.2
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pp.311-326
/
2024
The rapid development of neural network technology promotes the neural network model driven by big data to overcome the texture effect of complex objects. Due to the limitations in complex scenes, it is necessary to establish custom template matching and apply it to the research of many fields of computational vision technology. The dependence on high-quality small label sample database data is not very strong, and the machine learning system of deep feature connection to complete the task of texture effect inference and speculation is relatively poor. The style transfer algorithm based on neural network collects and preserves the data of patterns, extracts and modernizes their features. Through the algorithm model, it is easier to present the texture color of patterns and display them digitally. In this paper, according to the texture effect reasoning of custom template matching, the 3D visualization of the target is transformed into a 3D model. The high similarity between the scene to be inferred and the user-defined template is calculated by the user-defined template of the multi-dimensional external feature label. The convolutional neural network is adopted to optimize the external area of the object to improve the sampling quality and computational performance of the sample pyramid structure. The results indicate that the proposed algorithm can accurately capture the significant target, achieve more ablation noise, and improve the visualization results. The proposed deep convolutional neural network optimization algorithm has good rapidity, data accuracy and robustness. The proposed algorithm can adapt to the calculation of more task scenes, display the redundant vision-related information of image conversion, enhance the powerful computing power, and further improve the computational efficiency and accuracy of convolutional networks, which has a high research significance for the study of image information conversion.
Kim, Jong-Rae;Jung, Chang-Ho;Kim, Yong-Ho;Yoon, Jong-Man
Development and Reproduction
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v.10
no.4
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pp.227-238
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2006
Genomic DNAs(gDNAs) were isolated from the venus clam(Gomphina aequilatera) from Samcheok(venus clam from Samcheok; VCS) and Wonsan(venus clam from Wonsan; VCW) located in the East Sea of the Korean Peninsula. The amplified products were generated by agarose gel electrophoresis(AGE) with oligonucleotides primer, detected by staining with ethidium bromide and viewed by ultraviolet ray. The seven arbitrarily selected primers BION-21, BION-23, BION-25, BION-27, BION-29, BION-31 and BION-33 generated the shared loci, polymorphic, and specific loci, with the molecular sizes ranging from 150 bp to 2,400 bp. In this study, 147 polymorphic loci(147/954 loci, 15.41%) in VCS population and 274(274/996 loci, 27.51%) in VCW population were generated with seven primers. These results suggest the genetic variation in VCW population is higher than in VCS population. Especially, the 700 bp bands generated by the primer BION-21 were identified commonly in two Gomphina populations, which identified populations and/or species. This specific primer was found to be useful in the identification of individuals and/or population, resulting from the different DNA polymorphism among individuals/species/population. Two Gomphina populations between the individual SAMCHEOK no. 03 and WONSAN no. 22 showed the longest genetic distance(0.696) in comparison with other individuals used. The complete linkage cluster analysis indicating three genetic groupings and dendrogram revealed close relationships among individual identities within two geographical populations of venus clam(G. aequilatera) from the Samcheok and Wonsan. The intra-species classification and clustering analyses inferred from molecular markers supported the traditional taxonomy of the species based on morphological characters such as shell size, shape and color. Accordingly, as mentioned above, RAPD analysis showed that VCS population was more or less separated from VCW population.
Journal of the Institute of Electronics Engineers of Korea SP
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v.41
no.5
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pp.45-52
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2004
According tothe development of digital media technologies various algorithms for video sequence matching have been proposed to match the video sequences efficiently. A large number of video sequence matching methods have focused on frame-wise query, whereas a relatively few algorithms have been presented for video sequence matching or video shot matching. In this paper, we propose an efficientalgorithm to index the video sequences and to retrieve the sequences for video sequence query. To improve the accuracy and performance of video sequence matching, we employ the Cauchy function as a similarity measure between histograms of consecutive frames, which yields a high performance compared with conventional measures. The key frames extracted from segmented video shots can be used not only for video shot clustering but also for video sequence matching or browsing, where the key frame is defined by the frame that is significantly different from the previous fames. Several key frame extraction algorithms have been proposed, in which similar methods used for shot boundary detection were employed with proper similarity measures. In this paper, we propose the efficient algorithm to extract key frames using the cumulative Cauchy function measure and. compare its performance with that of conventional algorithms. Video sequence matching can be performed by evaluating the similarity between data sets of key frames. To improve the matching efficiency with the set of extracted key frames we employ the Cauchy function and the modified Hausdorff distance. Experimental results with several color video sequences show that the proposed method yields the high matching performance and accuracy with a low computational load compared with conventional algorithms.
The morphological characteristics and genetic relationships among 32 germplasms of Zanthoxylum schinifolium and Zanthoxylum piperitum collected from two farms in Korea were investigated. The traits with the most variability were seed color, leaf size, and spine size. The intraspecific polymorphism of Z. schinifolium and Z. piperitum was 96.5% and 60.3%, respectively. The genetic diversity and Shannon’s information index values ranged from 0.11 to 0.33 and 0.19 to 0.50, with average values of 0.26 and 0.42, respectively. Two ISSR primers (UBC861 and UBC862) were able to distinguish the different species. The genetic similarity matrix (GSM) revealed variability among the accessions ranging from 0.116 to 0.816. The intraspecific GSM for Z. schinifolium and Z. piperitum was 0.177-0.780 and 0.250-0.816, respectively. The GSM findings indicate that Z. schinifolium and Z. piperitum accessions have high genetic diversity and possess germplasms qualifying as good genetic resources for cross breeding. The clustering analysis separated Z. schinifolium and Z. piperitum into independent groups, and all accessions could be classified into three categories. Z. Schinifolium var. nermis belonged to independent groups. Comparison of the clusters based on morphological analysis with those based on ISSR data resulted in an unclear pattern of division among the accessions. The study findings indicate that Z. schinifolium and Z. piperitum accessions have genetic diversity, and ISSR markers were useful for identifying Z. schinifolium and Z. piperitum.
O, Chang-Yun;Im, Dong-Ju;O, Gun-Seok;Bae, Sang-Hyeon
The KIPS Transactions:PartD
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v.8D
no.5
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pp.507-512
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2001
Feature-based similarity has become an important research issue in multimedia database systems. The features of multimedia data are useful for discriminating between multimedia objects. the performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increase. The $R^{*}-Tree$ is the most successful variant of the R-Tree. In this paper, we propose a SOM-based $R^{*}-Tree$ as a new indexing method for high-dimensional feature vectors. The SOM-based $R^{*}-Tree$ combines SOM and $R^{*}-Tree$ to achieve search performance more scalable to high-dimensionalties. Self-Organizingf Maps (SOMs) provide mapping from high-dimensional feature vectors onto a two-dimensional space. The map is called a topological feature map, and preserves the mutual relationships (similarity) in the feature spaces of input data, clustering mutually similar feature vectors in neighboring nodes. Each node of the topological feature map holds a codebook vector. We experimentally compare the retrieval time cost of a SOM-based $R^{*}-Tree$ with of an SOM and $R^{*}-Tree$ using color feature vectors extracted from 40,000 images. The results show that the SOM-based $R^{*}-Tree$ outperform both the SOM and $R^{*}-Tree$ due to reduction of the number of nodes to build $R^{*}-Tree$ and retrieval time cost.
The Journal of Korea Institute of Information, Electronics, and Communication Technology
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v.13
no.3
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pp.197-205
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2020
Korean language has the characteristics that the pronunciation of phoneme units such as vowels and consonants are fixed and the pronunciation associated with a notation does not change, so that foreign learners can approach rather easily Korean language. However, when one pronounces words, phrases, or sentences, the pronunciation changes in a manner of a wide variation and complexity at the boundaries of syllables, and the association of notation and pronunciation does not hold any more. Consequently, it is very difficult for foreign learners to study Korean standard pronunciations. Despite these difficulties, it is believed that systematic analysis of pronunciation errors for Korean words is possible according to the advantageous observations that the relationship between Korean notations and pronunciations can be described as a set of firm rules without exceptions unlike other languages including English. In this paper, we propose a visualization framework which shows the differences between standard pronunciations and erratic ones as quantitative measures on the computer screen. Previous researches only show color representation and 3D graphics of speech properties, or an animated view of changing shapes of lips and mouth cavity. Moreover, the features used in the analysis are only point data such as the average of a speech range. In this study, we propose a method which can directly use the time-series data instead of using summary or distorted data. This was realized by using the deep learning-based technique which combines Self-organizing map, variational autoencoder model, and Markov model, and we achieved a superior performance enhancement compared to the method using the point-based data.
Eunju Kim;Tae-Mun Hwang;Jae-Wuk Koo;Jaeyong Song;Hongkyeong Park;Sookhyun Nam
Journal of Korean Society of Water and Wastewater
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v.37
no.6
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pp.425-435
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2023
Organoleptic parameters such as color, odor, and flavor influence consumer perception of drinking water quality. This study aims to evaluate the taste of the selected bottled and tap water samples using an electronic tongue (E-tongue) instead of a sensory test. Bottled and tap water's mineral components are related to the overall preference for water taste. Contrary to the sensory test, the potentiometric E-tongue method presented in this study distinguishes taste by measuring the mineral components in water, and the data obtained can be statistically analyzed. Eleven bottled water products from various brands and one tap water from I city in Korea were evaluated. The E-tongue data were statistically analyzed using multivariate statistical tools such as hierarchical clustering analysis (HCA), principal component analysis (PCA), and partial least squares discriminant analysis (PLS-DA). The results show that the E-tongue method can clearly distinguish taste discrimination in drinking water differing in water quality based on the ion-related water quality parameters. The water quality parameters that affect taste discrimination were found to be total dissolved solids (TDS), sodium (Na+), calcium (Ca2+), magnesium (Mg2+), sulfate (SO42-), chloride (Cl-), potassium (K+) and pH. The distance calculation of HCA was used to quantify the differences between 12 different types of drinking water. The proposed E-tongue method is a practical tool to quantitatively evaluate the differences between samples in water quality items related to the ionic components. It can be helpful in quality control of drinking water.
Zhao, Bi-Cheng;Rosli, Ahmad Nurzid;Jang, Chol-Hee;Lee, Kee-Sung;Jo, Geun-Sik
Journal of Intelligence and Information Systems
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v.18
no.1
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pp.1-21
/
2012
In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.
Journal of the Korean Institute of Landscape Architecture
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v.45
no.3
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pp.66-79
/
2017
In 2014, a Donghak Peasant Revolution Memorial Park design competition was held with various forms and techniques to convey mourning. This is a process of the reconsideration of memorial projects that are used to stimulate the collective memory and it is a meaningful resource for examining the consciousness of contemporary designers in regards to the memorial designs that are currently under planning in Korea. This study investigated the background of the Donghak commemorative projects that took place at the same site in a timely manner and analyzed the design competition through the existing literature research. Through this, it was seen that the memorial, which was formed by means of past political purposes, has changed into a way to collect various opinions and forms through open design competition. A framework of analysis prepared through multi-layer analysis is daily use, interaction and spontaneity, abstraction, temporality, locality, integration and harmony with surroundings. The results of this study are as follows. First, in order to convey memorial commemoration in everyday life, the projects organized scattered memorial spaces with special characteristics and linked them with daily activities program. Second, the projects used direct participation and emotional experiences to interact with monuments. Third, color, vertical elements, clustering, and park frame manipulation were used for abstract reproduction. Fourth, the projects introduce architecture and furniture that can be changed and plants for temporal change. Fifth, the previous terrain was restored and the setting of the scene was reproduced in order to make the site a space with place. Sixth, to improve the connection with existing monuments, the projects used techniques such as relaxation and the reinforcement of circulation lines and axes. Seventh, a path and a building conforming to the terrain were arranged for harmony with the surroundings.
Journal of the Korean Society of Food Science and Nutrition
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v.35
no.2
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pp.199-204
/
2006
This study was conducted to investigate the quality characteristics of noodles prepared with the addition of nanofiltered (NF) powder of sunmul. Noodles were prepared with different levels $(0\%,\;1.5\%,\;3\%\;and\;5\%,\;w/w)$ of NF powder and physico-chemical properties were examined. Results of rapid visco analyzer showed that peak, trough, final viscosity and set back decreased as the NF powder level increased. The weight and volume of cooked noodles increased with the addition of NF powder. Turbidity of soup also increased as the amount of NF powder increased, indicating higher cooking loss. The color of wet and cooked noodles became greenish yellow as the NF powder level increased. Hardness, springiness, gumminess and brittleness of cooked noodles decreased with the increasing amount of NF powder. Results of sensory evaluation showed that noodles prepared with up to $3\%$ addition of NF powder was considered to be as acceptable as noodles prepared without NF powder.
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