• Journal of Korean Society of Coastal and Ocean Engineers
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• v.25 no.6
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• pp.374-385
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• 2013

Sediment particle motions have been numerically simulated over a sinusoidal ripple. Turbulent boundary layer flows are generated by Large Eddy Simulation, and the sediment particle motions are simulated using Lagrangian particle tracking method. Two unsteady flow conditions are used in the experiment by employing two different wave amplitudes while keeping other conditions such as wave period same. As expected, the amount of suspended sediment particles is clearly dependent on the wave amplitude as it is increasing with increasing flow intensity. However, it is also observed that the pattern of suspension may be different as well due to the only different condition caused by wave amplitude. Specially, the time of maximum sediment suspension within the wave period is not coincident between the two cases because sediment suspension is strongly affected by the existence of turbulent eddies that are formed at different times over the ripple between the two cases as well. The role of these turbulent eddies on sediment suspension is important as it is also confirmed in previous researches. However, it is also found the time of these eddies' formation may also dependent on the wave amplitude over rippled beds. Therefore, it has been proved that various flow as well as geometric conditions under waves has to be considered in order to have better understanding on the sediment suspension process over ripples. In addition, it is found that high turbulent energy and strong upward flow velocities occur during the time of eddy formation, which also supports high suspension rate at these time steps. The results indicate that the relationship between the structure of flows and bedforms has to be carefully examined in studying sediment suspension at coastal regions.

• Ecology and Resilient Infrastructure
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• v.6 no.4
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• pp.200-207
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• 2019

The movement distance and home range size of Pelophylax chosenicus were identified in the rice paddy and ecological park as alternative habitats from July to November 2017. A total of 39 frogs were tracked by radio tracking method. As a result, the average move distance in the population of rice paddy was 11.7 ± 1.9 m (n = 64) and the population of ecological park was 24.7 ± 4.3 m (n = 39). The move distance between the two populations was significantly different. The mean MCP of the population of rice paddy was 181.2 ± 110.8 m² (n = 11) and the population of ecological park was 416.1 ± 276.2 m² (n = 10), but there was no significant difference. The population area of rice paddy was 4,160 m² (Kernel density 95%) and the core area was 1,080 m² (Kernel density 50%). The population area (Kernel density 95%) of ecological park was 5,391 m² and the core area (Kernel density 50%) was 736 m². This study shows that it is appropriate to construct the area of alternative habitat for P. chosenicus at least 1.33 ha, and it is more advantageous for the ecological park to be constructed than the paddy field with high development pressure and human interference. If the rice paddies were to be abandoned for several years, or to be used traditional farming methods, such as refraining from using agricultural machinery and chemicals, they could be used as alternative habitat for P. chosenicus.

• The Korean Journal of Nuclear Medicine
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• v.39 no.3
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• pp.174-181
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• 2005

Purpose: Reduction of respiratory motion artifacts in PET images was studied using respiratory-gated PET (RGPET) with moving phantom. Especially a method of generating simulated helical CT images from 4D-CT datasets was developed and applied to a respiratory specific RGPET images for more accurate attenuation correction. Materials and Methods: Using a motion phantom with periodicity of 6 seconds and linear motion amplitude of 26 mm, PET/CT (Discovery ST: GEMS) scans with and without respiratory gating were obtained for one syringe and two vials with each volume of 3, 10, and 30 ml respectively. RPM (Real-Time Position Management, Varian) was used for tracking motion during PET/CT scanning. Ten datasets of RGPET and 4D-CT corresponding to every 10% phase intervals were acquired. from the positions, sizes, and uptake values of each subject on the resultant phase specific PET and CT datasets, the correlations between motion artifacts in PET and CT images and the size of motion relative to the size of subject were analyzed. Results: The center positions of three vials in RGPET and 4D-CT agree well with the actual position within the estimated error. However, volumes of subjects in non-gated PET images increase proportional to relative motion size and were overestimated as much as 250% when the motion amplitude was increased two times larger than the size of the subject. On the contrary, the corresponding maximal uptake value was reduced to about 50%. Conclusion: RGPET is demonstrated to remove respiratory motion artifacts in PET imaging, and moreover, more precise image fusion and more accurate attenuation correction is possible by combining with 4D-CT.

• Korean Journal of Environment and Ecology
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• v.33 no.1
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• pp.1-8
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• 2019

Pintail (Anas acuta) is the major wintering bird in South Korea and known as a major mediator of the highly pathogenic avian influenza (HPAI). Pintail migrates long distances between Russian Siberia and Korea. This species prefers a rice paddy area as their winter habitat. The purpose of this study is to provide the data necessary for the conservation and management of bird habitats in Korea by understanding the wintering home-range and habitat of pintail in Korea. We captured six pintails using a cannon-net in the winter of 2015 and attached the GPS-mobile phone based telemetry (WT-300) on them to study the wintering home-range and wintering habitat. We analyzed the tracking location data using ArcGIS 9.0 Animal Movement Extension and calculated Kernel Density Estimation (KDE) and Minimum Convex Polygon (MCP). The average home-range in the wintering ground analyzed by MCP was $677.3km^2$ (SD=130.2, n=6) while the maximum and minimum were $847.7km^2$ and $467.5km^2$, respectively. Extents of home-range analyzed by KDE were $194.7km^2$ (KDE 90%), $77.4km^2$ (KDE 70%), and $35.3km^2$ (KDE 50%). The pintails mostly used both sea and paddy field as habitat in the winter season and utilized paddy fields more during the nighttime and than the daytime. We concluded that the home-range and habitat of pintails in the winter could be used as the reference data for the preservation of species, management of habitats, and coping with a breakout of HPAI.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• Asia pacific journal of information systems
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• v.18 no.3
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• pp.123-145
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• 2008

Recently, as the information communication technology develops, the discussion regarding the ubiquitous environment is occurring in diverse perspectives. Ubiquitous environment is an environment that could transfer data through networks regardless of the physical space, virtual space, time or location. In order to realize the ubiquitous environment, the Pervasive Sensing technology that enables the recognition of users' data without the border between physical and virtual space is required. In addition, the latest and diversified technologies such as Context-Awareness technology are necessary to construct the context around the user by sharing the data accessed through the Pervasive Sensing technology and linkage technology that is to prevent information loss through the wired, wireless networking and database. Especially, Pervasive Sensing technology is taken as an essential technology that enables user oriented services by recognizing the needs of the users even before the users inquire. There are lots of characteristics of ubiquitous environment through the technologies mentioned above such as ubiquity, abundance of data, mutuality, high information density, individualization and customization. Among them, information density directs the accessible amount and quality of the information and it is stored in bulk with ensured quality through Pervasive Sensing technology. Using this, in the companies, the personalized contents(or information) providing became possible for a target customer. Most of all, there are an increasing number of researches with respect to recommender systems that provide what customers need even when the customers do not explicitly ask something for their needs. Recommender systems are well renowned for its affirmative effect that enlarges the selling opportunities and reduces the searching cost of customers since it finds and provides information according to the customers' traits and preference in advance, in a commerce environment. Recommender systems have proved its usability through several methodologies and experiments conducted upon many different fields from the mid-1990s. Most of the researches related with the recommender systems until now take the products or information of internet or mobile context as its object, but there is not enough research concerned with recommending adequate store to customers in a ubiquitous environment. It is possible to track customers' behaviors in a ubiquitous environment, the same way it is implemented in an online market space even when customers are purchasing in an offline marketplace. Unlike existing internet space, in ubiquitous environment, the interest toward the stores is increasing that provides information according to the traffic line of the customers. In other words, the same product can be purchased in several different stores and the preferred store can be different from the customers by personal preference such as traffic line between stores, location, atmosphere, quality, and price. Krulwich(1997) has developed Lifestyle Finder which recommends a product and a store by using the demographical information and purchasing information generated in the internet commerce. Also, Fano(1998) has created a Shopper's Eye which is an information proving system. The information regarding the closest store from the customers' present location is shown when the customer has sent a to-buy list, Sadeh(2003) developed MyCampus that recommends appropriate information and a store in accordance with the schedule saved in a customers' mobile. Moreover, Keegan and O'Hare(2004) came up with EasiShop that provides the suitable tore information including price, after service, and accessibility after analyzing the to-buy list and the current location of customers. However, Krulwich(1997) does not indicate the characteristics of physical space based on the online commerce context and Keegan and O'Hare(2004) only provides information about store related to a product, while Fano(1998) does not fully consider the relationship between the preference toward the stores and the store itself. The most recent research by Sedah(2003), experimented on campus by suggesting recommender systems that reflect situation and preference information besides the characteristics of the physical space. Yet, there is a potential problem since the researches are based on location and preference information of customers which is connected to the invasion of privacy. The primary beginning point of controversy is an invasion of privacy and individual information in a ubiquitous environment according to researches conducted by Al-Muhtadi(2002), Beresford and Stajano(2003), and Ren(2006). Additionally, individuals want to be left anonymous to protect their own personal information, mentioned in Srivastava(2000). Therefore, in this paper, we suggest a methodology to recommend stores in U-market on the basis of ubiquitous environment not using personal information in order to protect individual information and privacy. The main idea behind our suggested methodology is based on Feature Matrices model (FM model, Shahabi and Banaei-Kashani, 2003) that uses clusters of customers' similar transaction data, which is similar to the Collaborative Filtering. However unlike Collaborative Filtering, this methodology overcomes the problems of personal information and privacy since it is not aware of the customer, exactly who they are, The methodology is compared with single trait model(vector model) such as visitor logs, while looking at the actual improvements of the recommendation when the context information is used. It is not easy to find real U-market data, so we experimented with factual data from a real department store with context information. The recommendation procedure of U-market proposed in this paper is divided into four major phases. First phase is collecting and preprocessing data for analysis of shopping patterns of customers. The traits of shopping patterns are expressed as feature matrices of N dimension. On second phase, the similar shopping patterns are grouped into clusters and the representative pattern of each cluster is derived. The distance between shopping patterns is calculated by Projected Pure Euclidean Distance (Shahabi and Banaei-Kashani, 2003). Third phase finds a representative pattern that is similar to a target customer, and at the same time, the shopping information of the customer is traced and saved dynamically. Fourth, the next store is recommended based on the physical distance between stores of representative patterns and the present location of target customer. In this research, we have evaluated the accuracy of recommendation method based on a factual data derived from a department store. There are technological difficulties of tracking on a real-time basis so we extracted purchasing related information and we added on context information on each transaction. As a result, recommendation based on FM model that applies purchasing and context information is more stable and accurate compared to that of vector model. Additionally, we could find more precise recommendation result as more shopping information is accumulated. Realistically, because of the limitation of ubiquitous environment realization, we were not able to reflect on all different kinds of context but more explicit analysis is expected to be attainable in the future after practical system is embodied.

• Journal of the Korean Society of Radiology
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• v.5 no.4
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• pp.189-194
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• 2011

This study used magnetic resonance diffusion tensor imaging (DTI) to quantitatively analyze the neural fiber tractography according to the age of normal corpus callosum and to evaluate of usefulness. The research was intended for the applicants of 60 persons that was in a good state of health with not brain or other disease. The test parameters were TR: 6650 ms, TE: 66 ms, FA: $90^{\circ}$, NEX: 2, thickness: 2 mm, no gap, FOV: 220 mm, b-value: $800s/mm^2$, sense factor: 2, acquisition matrix size: $2{\times}2{\times}2mm^3$, and the test time was 3 minutes 46 seconds. The evaluation method was constructed the color-cored FA map include to the skull vertex from the skull base in scan range. We set up the five ROI of corpus callosum of genu, anterior-mid body, posterior-mid body, isthmus, and splenium, tracking, respectively, and to quantitatively measured the length of neural fiber. As a result, the length of neural fiber, for the corpus callosum of genu was 20's: $61.8{\pm}6.8$, 30's: $63.9{\pm}3.8$, 40's: $65.5{\pm}6.4$, 50's: $57.8{\pm}6.0$, 60's: $58.9{\pm}4.5$, more than 70's: $54.1{\pm}8.1mm$, for the anterior-mid body was 20's: $54.8{\pm}8.8$, 30's: $58.5{\pm}7.9$, 40's: $54.8{\pm}7.8$, 50's: $56.1{\pm}10.2$, 60's: $48.5{\pm}6.2$, more than 70's: $48.6{\pm}8.3mm$, for the posterior-mid body was 20's: $72.7{\pm}9.1$, 30's: $61.6{\pm}9.1$, 40's: $60.9{\pm}10.5$, 50's: $61.4{\pm}11.7$, 60's: $54.9{\pm}10.0$, more than 70's: $53.1{\pm}10.5mm$, for the isthmus was 20's: $71.5{\pm}17.4$, 30's: $74.1{\pm}14.9$, 40's: $73.6{\pm}14.2$, 50's: $66.3{\pm}12.9$, 60's: $56.5{\pm}11.2$, more than 70's: $56.8{\pm}11.3mm$, and for the splenium was 20's: $82.6{\pm}6.8$, 30's: $86.9{\pm}6.4$, 40's: $83.1{\pm}7.1$, 50's: $81.5{\pm}7.4$, 60's: $78.6{\pm}6.0$, more than 70's: $80.55{\pm}8.6mm$. The length of neural fiber for normal corpus callosum were statistically significant in the genu(P=0.001), posterior-mid body(P=0.009), and istumus(P=0.012) of corpus callosum. In order of age, the length of neural fiber increased from 30s to 40s, as one grows older tended to decrease. For this reason, the nerve cells of brain could be confirmed through the neural fiber tractography to progress actively in middle age.