• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.1
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• pp.115-121
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• 2010

As internet becomes widespread, a lot of information is opened to public and users of Internet can access effectively information using web searching service. To construct web searching service, the web searching method for collecting of information is needed to obtain web page view. As a number of web page view increases, it is necessary to collect information of high quality information to be searched, therefore, a variety of web engine for searching mechanism is developed. Method of link extraction with javascript in dynamic web page and design of web searching robot are presented m this paper. To evaluate performance analyzes, we fixed one searching model with the proposed method. The searching time takes 2 minute 67 sec for 299 web pages and 12.33 sec for 10 searching model.

• Journal of Information Processing Systems
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• v.17 no.5
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• pp.1020-1033
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• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.

• Journal of the Korea Convergence Society
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• v.8 no.11
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• pp.271-277
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• 2017

In the image processing, foreground image extraction is mainly applied to recognize a moving object or an object. In the game, the objects included in the foreground image can be mainly characters, non player characters, items, and the like. These objects can be the player's primary concern with objects that are the target of players' movement, attack, defense, and collection. In this background, this research is a study to extract players' interest areas. To this end, first, the foreground image is extracted. Second, the extracted foreground image is accumulated for a certain period of time, and the image is displayed as a result image. The accumulated foreground image according to the play time helps to know the location and frequency of screen appearance of game objects. This study can help players design their interest areas and design an efficient UX/UI.

• Journal of the Society of Disaster Information
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• v.19 no.3
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• pp.689-697
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• 2023

Purpose: The purpose of this study is to automatically extract information on underground facilities using a general-purpose smartphone in the process of applying the mini-trenching method. Method: Data sets for image learning were collected under various conditions such as day and night, height, and angle, and the object detection algorithm used the R-CNN algorithm. Result: As a result of the study, F1-Score was applied as a performance evaluation index that can consider the average of accurate predictions and reproduction rates at the same time, and F1-Score was 0.76. Conclusion: The results of this study showed that it was possible to extract information on underground buried materials based on smartphones, but it is necessary to improve the precision and accuracy of the algorithm through additional securing of learning data and on-site demonstration.

• Journal of Digital Contents Society
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• v.18 no.7
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• pp.1341-1346
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• 2017

In this paper, we designed the open service platform based on integrated type of individual customized service and intelligent information technology with individual's complex attributes and requests. First, the data collection phase is proceed quickly and accurately to repeat extraction, transformation and loading. The generated data from extraction-transformation-loading process module is stored in the distributed data system. The data analysis phase is generated a variety of patterns that used the analysis algorithm in the field. The data processing phase is used distributed parallel processing to improve performance. The data providing should operate independently on device-specific management platform. It provides a type of the Open API.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.563-572
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• 2021

This study aims to investigate the implementation of village fund (VF) policy in Indonesia by addressing the following issues: (1) what is the VF policy; (2) factors that support and hinder policy implementation; (3) impact of policy implementation; and (4) model for implementing village fund policies that can improve community welfare. Through a descriptive qualitative-based approach, several indicators are measured, namely, the substance of implementing rules, the results of project implementation, supporting and inhibiting factors for policies, participation factors, and the impact generated by village fund policies, which include social and economic effects. The extraction of this information and indicators will lead this study to produce ideal models and propositions for quantitative confirmatory research as a future research agenda. This study was conducted in two villages (Mojomulyo and Tambakromo) in Pati District, Central Java, Indonesia. Data collection model using interviews and observations from all actors who play a role (e.g., village government, village supervisory agency, and community). The study results show that policies have been implemented by normative rules; there are several supporting and inhibiting factors both internal and external. The study results also confirm the relevance of the articulated theory and some comprehensive input to our study.

• Journal of Multimedia Information System
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• v.9 no.3
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• pp.201-208
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• 2022

Customized service is a vital and mandatory element for apps in improving their technical performance and app customer analysis. While apps require users' consent for their data extraction and usage, many of the terms and agreement forms are written intricately, making it harder for users to fully understand the whole concept of users' data collection for customized services. Ever since the Facebook-Cambridge Analytica scandal, personal data privacy has been re-examined, forcing many app companies to reinforce a reliable solution to data privacy issues. However, there has not been a secured solution, which worries many people about the future advanced issues when metaverse platforms are actively used in daily apps. The research aims to collect the reactions and behaviors of everyday app users who utilize apps with customized services to understand the nature of privacy data issues and the users' opinions about the future implementation of metaverse platforms. The method of the research was an online questionnaire that targeted university students. The study revealed many fearful and anxious reactions about personal data and further metaverse issues where most app users were uneducated about how current apps collect and utilize users' private data.

• Journal of Korean Academy of Nursing
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• v.42 no.4
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• pp.453-465
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• 2012

Purpose: The study was done to develop a shared decision-making scale for end-of-life patients in Korea. Methods: The process included construction of a conceptual framework, generation of initial items, verification of content validity, selection of secondary items, preliminary study, and extraction of final items. The participants were 388 adults who lived in one of 3 Korean metropolitan cities: Seoul, Daegu, or Busan. Item analysis, factor analysis, criterion related validity, and internal consistency were used to analyze the data. Data collection was done from July to October 2011. Results: Thirty-four items were selected for the final scale, and categorized into 7 factors explaining 61.9% of the total variance. The factors were labeled as sharing information (9 items), constructing system (7 items), explanation as a duty (5 items), autonomy (4 items), capturing time (3 items), participation of family (3 items), and human respect (3 items). The scores for the scale were significantly correlated among shared decision-making scale, terminating life support scale, and dignified dying scale. Cronbach's alpha coefficient for the 34 items was .94. Conclusion: The above findings indicate that the shared decision-making scale has a good validity and reliability when used for end-of-life patients in Korea.

• Journal of Korean Society for Geospatial Information Science
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• v.19 no.3
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• pp.23-32
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• 2011

This research uses a distributed model, Vflo which can devide subwater shed into square grids and interpret diverse topographic elements which are obtained through GIS processing. To use the distributed model, soil wetness information was extracted through Tasseled Cap transformation from LANDSAT 7 $ETM^+$ satellite data and then they were applied to each cell of the test area, unlike previous studies in which have applied average soil condition of river basin uniformly regardless of space-difference in subwater shed. As a resut of the research, it was ascertained the spatial change of soil wetness is suited to the distributed model in a subwater shed. In addition, we derived out a relation between soil wetness of image collection time and 10 days-preceded rainfall and improved the feasibility of weights obtained by the relation equation.

• KIPS Transactions on Computer and Communication Systems
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• v.1 no.1
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• pp.55-60
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• 2012

Recent botnets are widely using the DNS services at the connection of C&C server in order to evade botnet's detection. It is necessary to study on DNS analysis in order to counteract anomaly-based technique using the DNS. This paper studies collection of DNS traffic for experimental data and supervised learning for DNS traffic-based malicious domain classification such as query of domain name corresponding to C&C server from zombies. Especially, this paper would aim to determine significant features of DNS-based classification system for malicious domain extraction by the Principal Component Analysis(PCA).