• Journal of the Institute of Convergence Signal Processing
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• v.9 no.1
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• pp.31-38
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• 2008

Ad-Hoc network is a network architecture which has no backbone network and is deployed temporarily and rapidly in emergency or war without fixed mobile infrastructures. All communications between network entities are carried in ad-hoc networks over the wireless medium. Due to the radio communications being extremely vulnerable to propagation impairments, connectivity between network nodes is not guaranteed. Therefore, many new algorithms have been studied recently. This study proposes the secondary header approach to the cluster based routing protocol (CBRP). The primary header becomes abnormal status so that the primary header can not participate in the communications between network entities, the secondary header immediately replaces the primary header without selecting process of the new primary header. This improves the routing interruption problem that occurs when a header is moving out from a cluster or in the abnormal status. The performances of proposed algorithm ACBRP(Advanced Cluster Based Routing Protocol) are compared with CBRP. The cost of the primary header reelection of ACBRP is simulated. And results are presented in order to show the effectiveness of the algorithm.

• Geophysics and Geophysical Exploration
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• v.9 no.4
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• pp.271-278
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• 2006

Investigations of underground cavities are required to provide useful information for the reinforcement design and monitoring of the ground subsidence areas. It is, therefore, necessary to develop integrated geophysical techniques incorporating different geophysical methods in order to accurately image and to map underground cavities in the ground subsidence areas. In this study, we conducted geophysical investigations for development of integrated geophysical techniques to detect underground cavities at the field test site in the ground subsidence area, located at Yongweol-ri, Muan-eup, Muan-gun, Jeollanam-do. We examined the applicability of geophysical methods such as electrical resistivity, electromagnetic, and microgravity to cavity detection with the aid of borehole survey results. The underground cavities are widely present within the limestone bedrock overlain by the alluvial deposits in the test site where the ground subsidences have occurred in the past. The limestone cavities are mostly filled with groundwater or clays saturated with water in the site. The cavities, thus, have low electrical resistivity and density compared to the surrounding host bedrock. The results of the study have shown that the zones of low resistivity and density correspond to the zones of the cavities identified in the boreholes at the site, and that the geophysical methods used are very effective to detect the underground cavities. Furthermore, we could map the distribution of cavities more precisely with the study results incorporated from the various geophysical methods. It is also important to notice that the microgravity method, which has rarely used in Korea, is a very promising tool to detect underground cavities.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.4
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• pp.89-101
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• 2017

Crime occurs differently based on not only place locations and building uses but also the characteristics of the people who use the place and the spatial structures of the buildings and locations. Therefore, if spatial big data, which contain spatial and regional properties, can be utilized, proper crime prevention measures can be enacted. Recently, with the advent of big data and the revolutionary intelligent information era, predictive policing has emerged as a new paradigm for police activities. Based on 7420 actual crime incidents occurring over three years in a typical provincial city, "J city," this study identified the areas in which crimes occurred and predicted risky areas. Spatial regression analysis was performed using spatial big data about only physical and environmental variables. Based on the results, using the street width, average number of building floors, building coverage ratio, the type of use of the first floor (Type II neighborhood living facility, commercial facility, pleasure use, or residential use), this study established a Crime Incident Prediction Model (CIPM) based on Bayesian probability theory. As a result, it was found that the model was suitable for crime prediction because the overlap analysis with the actual crime areas and the receiver operating characteristic curve (Roc curve), which evaluated the accuracy of the model, showed an area under the curve (AUC) value of 0.8. It was also found that a block where the commercial and entertainment facilities were concentrated, a block where the number of building floors is high, and a block where the commercial, entertainment, residential facilities are mixed are high-risk areas. This study provides a meaningful step forward to the development of a crime prediction model, unlike previous studies that explored the spatial distribution of crime and the factors influencing crime occurrence.

• Journal of the Korean Geographical Society
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• v.51 no.5
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• pp.613-632
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• 2016

The recent diagnosis of the Anthropocene challenges public understanding of nature as a pure and singular entity removed from society, as the diagnosis confirms the earth-changing force of humans. In geography, the nature-society divide has been critically interrogated long before the diagnosis of the Anthropocene, developing several ways of theorizing nature-society relations. This paper introduces a new frontier for such theoretical endeavors: more-than-human geography. Inspired by the material and performative turn in geography and the social sciences around the 2000s, more-than-human geographers have sought to re-engage with the livingness of the world in the study of nature-society relations. Drawing on actor-network theory, non-representational theory (NRT) and vitalism, they have developed innovative ways of thinking about and relating to nature through the key concepts of 'nonhuman agency' and 'affect'. While more-than-human geography has been extensively debated and developed in recent Euro-American scholarship on cultural and economic geography, it has so far received limited attention in Korean geographical studies on nature. This paper aims to address this gap by discussing the key concepts and seminal work of more-than-human geography. I first outline four theoretical strands through which nature-society relations are perceived in geography. I then offer an overview of more-than-human geography, discussing its theoretical foundations and considering ontologies, epistemologies, politics and ethics associated with nature-society relations. Then, I compare more-than-human geography with political ecology, which is the mainstream critical approach in contemporary environmental social sciences. I would argue that more-than-human geography further challenges and develops political ecology through its heightened attention to the affective capacity of nonhumans and the methodological ethos of doing a careful political ecology. I conclude by reflecting on the implications of more-than-human geography for Korean studies on nature-society relations.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.159-172
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• 2010

The recommender system is one of the possible solutions to assist customers in finding the items they would like to purchase. To date, a variety of recommendation techniques have been developed. One of the most successful recommendation techniques is Collaborative Filtering (CF) that has been used in a number of different applications such as recommending Web pages, movies, music, articles and products. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. Broadly, there are memory-based CF algorithms, model-based CF algorithms, and hybrid CF algorithms which combine CF with content-based techniques or other recommender systems. While many researchers have focused their efforts in improving CF performance, the theoretical justification of CF algorithms is lacking. That is, we do not know many things about how CF is done. Furthermore, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting the performances of CF algorithms in advance is practically important and needed. In this study, we propose an efficient approach to predict the performance of CF. Social Network Analysis (SNA) and Artificial Neural Network (ANN) are applied to develop our prediction model. CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. SNA facilitates an exploration of the topological properties of the network structure that are implicit in data for CF recommendations. An ANN model is developed through an analysis of network topology, such as network density, inclusiveness, clustering coefficient, network centralization, and Krackhardt's efficiency. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Inclusiveness refers to the number of nodes which are included within the various connected parts of the social network. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. Krackhardt's efficiency characterizes how dense the social network is beyond that barely needed to keep the social group even indirectly connected to one another. We use these social network measures as input variables of the ANN model. As an output variable, we use the recommendation accuracy measured by F1-measure. In order to evaluate the effectiveness of the ANN model, sales transaction data from H department store, one of the well-known department stores in Korea, was used. Total 396 experimental samples were gathered, and we used 40%, 40%, and 20% of them, for training, test, and validation, respectively. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. The input variable measuring process consists of following three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used Net Miner 3 and UCINET 6.0 for SNA, and Clementine 11.1 for ANN modeling. The experiments reported that the ANN model has 92.61% estimated accuracy and 0.0049 RMSE. Thus, we can know that our prediction model helps decide whether CF is useful for a given application with certain data characteristics.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.21-41
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• 2010

This paper proposes a system that can serve users with appropriate search results through real time filtering, and implemented adaptive user profiling based personalized information retrieval system(PIRS) using users' implicit feedbacks in order to deal with the problem of existing search systems such as Google or MSN that does not satisfy various user' personal search needs. One of the reasons that existing search systems hard to satisfy various user' personal needs is that it is not easy to recognize users' search intentions because of the uncertainty of search intentions. The uncertainty of search intentions means that users may want to different search results using the same query. For example, when a user inputs "java" query, the user may want to be retrieved "java" results as a computer programming language, a coffee of java, or a island of Indonesia. In other words, this uncertainty is due to ambiguity of search queries. Moreover, if the number of the used words for a query is fewer, this uncertainty will be more increased. Real-time filtering for search results returns only those results that belong to user-selected domain for a given query. Although it looks similar to a general directory search, it is different in that the search is executed for all web documents rather than sites, and each document in the search results is classified into the given domain in real time. By applying information filtering using real time directory classifying technology for search results to personalization, the number of delivering results to users is effectively decreased, and the satisfaction for the results is improved. In this paper, a user preference profile has a hierarchical structure, and consists of domains, used queries, and selected documents. Because the hierarchy structure of user preference profile can apply the context when users perfomed search, the structure is able to deal with the uncertainty of user intentions, when search is carried out, the intention may differ according to the context such as time or place for the same query. Furthermore, this structure is able to more effectively track web documents search behaviors of a user for each domain, and timely recognize the changes of user intentions. An IP address of each device was used to identify each user, and the user preference profile is continuously updated based on the observed user behaviors for search results. Also, we measured user satisfaction for search results by observing the user behaviors for the selected search result. Our proposed system automatically recognizes user preferences by using implicit feedbacks from users such as staying time on the selected search result and the exit condition from the page, and dynamically updates their preferences. Whenever search is performed by a user, our system finds the user preference profile for the given IP address, and if the file is not exist then a new user preference profile is created in the server, otherwise the file is updated with the transmitted information. If the file is not exist in the server, the system provides Google' results to users, and the reflection value is increased/decreased whenever user search. We carried out some experiments to evaluate the performance of adaptive user preference profile technique and real time filtering, and the results are satisfactory. According to our experimental results, participants are satisfied with average 4.7 documents in the top 10 search list by using adaptive user preference profile technique with real time filtering, and this result shows that our method outperforms Google's by 23.2%.

• Food Science and Preservation
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• v.20 no.4
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• pp.554-563
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• 2013

The changes in quality properties and nutritional components for two mugworts, namely, Artemisia capillaris Thumberg Artemisiae asiaticae Nakai fermented by Bacillus strains were characterized followed by rapid pattern analysis of volatile flavor compounds through the SAW-based electronic nose sensor in the GC system. After fermentation, the pH has remarkably decreased from 6.0~6.4 to 4.6~5.1 and there has been a slight change in the total soluble solids. The L (lightness) and b (yellowness) values in the Hunter's color system significantly decreased, whilst the a (redness) value increased via fermentation. The HPLC analysis demonstrated that the total amino acids increased in quantity and the essential amino acids were higher in the A. asiaticae Nakai than in the A. capillaris Thumberg, specially with high contents of glutamic and aspartic acid. After fermentation, the monounsaturated fatty acid increased in the A. asiaticae Nakai and the polyunsaturated fatty acids increased in the A. capillaris Thumberg. While the total polyphenol contents have not been affected by fermentation, the total sugar contents have dramatically decreased. Scopoletin, which is one of the most important index components in mugworts, was highly abundant in the A. capillaris Thumberg; however, it was not detected in the A. asiaticae Nakai. Small pieces of plant tissue in the surface microstructure were found in the fermented mugworts through the use of the scanning electron microscope (SEM). Volatile flavor compounds via electronic nose showed that the intensity of several peaks has increased and additional seven flavor peaks have been produced after fermentation. The VaporPrintTM images demonstrated a notable difference in flavors between the A. asiaticae Nakai and A. capillaris Thumberg, and the fermentation enabled the mugworts to produce subtle differences in flavor.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2003.05a
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• pp.181-186
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• 2003

The satellite navigation system is widely used for identifying a user's position regardless of weather or geographic conditions and also make effect on new technology of marine LBS(Location Based Service), which has the technology of geographic information such as the ENC. Generally, there are conceivable systems of marine LBS such as ECDIS, or ECS that use the ENC itself with powerful processor in installed type on ships bridge. Since the ENC is relatively heavy structure with dummy format for data transfer between different systems, we should reduce the ENC to small and compact size in order to use it in mobile platform. In this paper, we assumed that the mobile system like PDA, or Webpad can be used for small capability of mobile platform. However, the ENC should be updated periodically by update profile data produced by HO. If we would reduce the ENC without a consideration of update, we could not get newly updated data furthermore. As summary, we studied considerations for ENC reduction with update capability. It will make the ENC be useful in many mobile platforms for various applications.

• Journal of Life Science
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• v.23 no.11
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• pp.1325-1335
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• 2013

Working dogs, such as rescue dogs, military watch dogs, guide dogs, and search dogs, are selected by in-training examination of desired traits, including concentration, possessiveness, and boldness. In recent years, genetic information has been considered to be an important factor for the outstanding abilities of working dogs. To characterize the molecular features of the canine genes related to phenotypes for working dogs, we investigated the 24 previously reported genes (AR, BDNF, DAT, DBH, DGCR2, DRD4, MAOA, MAOB, SLC6A4, TH, TPH2, IFT88, KCNA3, TBR2, TRKB, ACE, GNB1, MSTN, PLCL1, SLC25A22, WFIKKN2, APOE, GRIN2B, and PIK3CG) that were categorized to personality, olfactory sense, and athletic/learning ability. We analyzed the chromosomal location, gene-gene interactions, Gene Ontology, and expression patterns of these genes using bioinformatic tools. In addition, variable numbers of tandem repeat (VNTR) or microsatellite (MS) polymorphism in the AR, MAOA, MAOB, TH, DAT, DBH, and DRD4 genes were reviewed. Taken together, we suggest that the genetic background of the canine genes associated with various working dog behaviors and skill performance attributes could be used for proper selection of superior working dogs.

• Journal of KIISE:Databases
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• v.28 no.4
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• pp.529-544
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• 2001

Prefetching is an effective method to minimize the number of roundtrips between the client and the server in database management systems. In this paper we propose new notions of the type-level access pattern and the type-level access locality and developed an efficient prefetchin policy based on the notions. The type-level access patterns is a sequence of attributes that are referenced in accessing the objects: the type-level access locality a phenomenon that regular and repetitive type-level access patterns exist. Existing prefetching methods are based on object-level or page-level access patterns, which consist of object0ids of page-ids of the objects accessed. However, the drawback of these methods is that they work only when exactly the same objects or pages are accessed repeatedly. In contrast, even though the same objects are not accessed repeatedly, our technique effectively prefetches objects if the same attributes are referenced repeatedly, i,e of there is type-level access locality. Many navigational applications in Object-Relational Database Management System(ORDBMs) have type-level access locality. Therefore our technique can be employed in ORDBMs to effectively reduce the number of roundtrips thereby significantly enhancing the performance. We have conducted extensive experiments in a prototype ORDBMS to show the effectiveness of our algorithm. Experimental results using the 007 benchmark and a real GIS application show that our technique provides orders of magnitude improvements in the roundtrips and several factors of improvements in overall performance over on-demand fetching and context-based prefetching, which a state-of the art prefetching method. These results indicate that our approach significantly and is a practical method that can be implemented in commercial ORDMSs.