• Journal of Korean Library and Information Science Society
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• v.37 no.2
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• pp.127-144
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• 2006

This study intends to analyze the integrated search methods in digital libraries environment. To categorize various integration methods, I investigated the developmental properties and integration types of digital libraries from a users point of view. And then, I reviewed the current best practices of library portal which are JISC Information Environment, NSDL OCKHAM and Korea Knowledge Portal Initiatives. Nextly, I derived the two methods of Integrated search mechanisms such as aggregated search and distributed search that is useful in the digital libraries environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.11
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• pp.3937-3954
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• 2014

With the increasing electricity consumption and the wide application of renewable energy sources, energy auction attracts a lot of attention due to its economic benefits. Many schemes have been proposed to support energy auction in smart grid. However, few of them can achieve range query, ranked search and personalized search. In this paper, we propose an efficient multi-keyword range query (EMRQ) scheme, which can support range query, ranked search and personalized search simultaneously. Based on the homomorphic Paillier cryptosystem, we use two super-increasing sequences to aggregate multidimensional keywords. The first one is used to aggregate one buyer's or seller's multidimensional keywords to an aggregated number. The second one is used to create a summary number by aggregating the aggregated numbers of all sellers. As a result, the comparison between the keywords of all sellers and those of one buyer can be achieved with only one calculation. Security analysis demonstrates that EMRQ can achieve confidentiality of keywords, authentication, data integrity and query privacy. Extensive experiments show that EMRQ is more efficient compared with the scheme in [3] in terms of computation and communication overhead.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3272-3285
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• 2016

Recently, Vector of Locally Aggregated Descriptors (VLAD) has been proposed to index image by compact representations, which encodes powerful local descriptors and makes significant improvement on search performance with less memory compared against the state of art. However, its performance relies heavily on the size of the codebook which is used to generate VLAD representation. It indicates better accuracy needs higher dimensional representation. Thus, more memory overhead is needed. In this paper, we enhance VLAD image representation by using two level hierarchical-codebooks. It can provide more accurate search performance while keeping the VLAD size unchanged. In addition, hierarchical-codebooks are used to construct multiple inverted files for more accurate non-exhaustive search. Experimental results show that our method can make significant improvement on both VLAD image representation and non-exhaustive search.

• The Journal of the Acoustical Society of Korea
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• v.40 no.5
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• pp.496-502
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• 2021

Recently, deep speaker embedding approach has been widely used in text-independent speaker verification, which shows better performance than the traditional i-vector approach. In this work, to improve the deep speaker embedding approach, we propose a novel method called group-based speaker embedding which incorporates group information. We cluster all speakers of the training data into a predefined number of groups in an unsupervised manner, so that a fixed-length group embedding represents the corresponding group. A Group Decision Network (GDN) produces a group weight, and an aggregated group embedding is generated from the weighted sum of the group embeddings and the group weights. Finally, we generate a group-based embedding by adding the aggregated group embedding to the deep speaker embedding. In this way, a speaker embedding can reduce the search space of the speaker identity by incorporating group information, and thereby can flexibly represent a significant number of speakers. We conducted experiments using the VoxCeleb1 database to show that our proposed approach can improve the previous approaches.

• The Journal of Korean Society for School & Community Health Education
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• v.11 no.1
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• pp.67-78
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• 2010

Background & Objectives: Current trend is that funding agencies require investigators to share their data with others. However, there is limited guidance how to access and utilize the shared data. We sought to determine what common data sharing practices in U.S.A. are, what data-related to adolescent health are freely available, and how we deal with the large dataset adopting the complex study design. Methods: The study included only research data-related to adolescent health which was collected in USA and unlimitedly accessible through the internet. Only the raw data, not aggregated, was considered for the study. Major keywords for web search were "adolescent", "children", "health", and "school". Results: Current approaches for public health data sharing lacked of common standards and varied largely due to the data's complex nature, large size, local expertise and internal procedures. Some common data sharing practices are unlimited access, formal screened access, restricted access, and informal exclusive access. The Inter-University Consortium for Political and Social Research and the Center for Disease Control and Prevention were the best data depository. "Data on the net" was search engine for the website providing data freely available. Six datasets related to adolescent health freely available were identified. The importance and methods of incorporating complex research design into analysis was discussed. Conclusion: There have been various attempts to standardize process for open access and open data using the information technology concept. However, it may not be easy for researchers to adapt themselves to this high technology. Therefore, guidance provided by this study may help researchers enhance the accessibility to and the utilization of the open source data.

• Journal of Environmental Health Sciences
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• v.44 no.6
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• pp.591-598
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• 2018

Objectives: Radiologists are the service worker with emotional labor who works within a relatively small space of the hospital, dealing with multiple patients. The purposes of this study were to search the turnover intention and job satisfaction through the work of emotional labor of radiologists. Methods: A questionnaire survey was conducted on 109 radiologists working at university hospitals, general hospitals, clinics and other facilities across the domestic. The regions were aggregated according to Quality Function Deployment (QFD) method, which was applied in proportion to the population distribution. A questionnaire was composed of 26 questions which were used in Korean Emotional Laboratory Scale (K-ELS). Results: The overall emotional labor score was $48.3{\pm}15.8$. The most pressing requirement of radiologists to improve emotional labor was the topic about the exchange of colleagues ($75.8{\pm}15.6$ score, ${\beta}=0.78$, p<0.001). Conclusions: In order to resolve the emotional labor of radiologists, it could be necessary to have a meeting through training and workshops where the exchange of colleagues can take place.

• The Transactions of the Korea Information Processing Society
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• v.5 no.6
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• pp.1404-1417
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• 1998

Currently, there exists two different methods for database retrieval in the internet. First is to use the search engine and the second is to use the plug-in or ActiveX technology, If a search engine, which makes use of indices built from keywords of simple text data in order to do a search, is used when accessing a database, first it is not possible to access more than one database at a time, second it is also not possible to support various conditional retrievals as in using query language, and third the set of data received might include many unwanted data, in other words, precision rate might be relatively low. Plug in or Active technology make use of Web browset to execute chents' query in order to do a database retrieval. Problems associated with this is that it is not possible to activate more than one DBMS simultaneously even if they are of the same data model. sefond it is not possible to execute a user query other than the ones thai arc previou sly defined by the client program In this paper, to resolve those aforementioned problems we design and implement database retrieval system using a virtual database, which makes it possible to provide direct query jntertacc through the conventional Web browser. We assume that the virtual database is designed and aggregated from more than one relational database using the same data model.

• Clinical and Experimental Reproductive Medicine
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• v.48 no.4
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• pp.311-315
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• 2021

Objective: As clinicians and patients await consensus on intraovarian platelet-rich plasma (PRP) treatment, this project evaluated contemporary research trends in the literature. Methods: A PubMed/NLM search aggregated all ovarian PRP-related publications (n=54) to evaluate their scope, abstract utility, submission-to-publication interval, journal selected, article processing charge (APC), free reader access to full-text manuscripts, number and nationality of authors, and inclusion of international collaborators. The NIH Clinical Trials database was also audited. Results: Published output on intraovarian PRP has increased consistently since 2016, especially among investigators in Greece, Iran, USA, and Turkey. Between 2013 and 2021, 42 articles met the relevancy criteria, of which 40.5% reported clinical studies, small series, or case reports, 33% described experimental animal models, and 23.8% were opinion/review papers. Only two works included a placebo control group. The submission-to-publication interval (mean±standard deviation) was 130±96 days, there were 5.9±3.2 authors per project, and journals invoiced US $1,613±1,466 (range, $0-$3,860) for APCs. Conclusion: There was no correlation between APC and time to publish (Pearson's r=-0.01). Abstract content was inconsistent; sample size and patient age were often missing, yet free full-text "open access" was available for most publications (59.5%). The NIH Clinical Trials portal lists eight registered studies on "ovarian rejuvenation," of which two are actively recruiting patients, while four have been terminated or have an uncertain status. Two studies have concluded, with results from one posted to the NIH website. PRP and its derivatives for ovarian treatment show early promise, but require further investigation. Research is accelerating and should be encouraged, particularly placebo-controlled randomized clinical trials.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.999-1005
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• 2020

While search portals' 'Portal News' account for the largest portion of aggregated news outlet, its neutrality as an outlet is questionable. This is because news aggregation may lead to prejudiced information consumption by recommending biased news articles. In this paper we introduce a new method of measuring political bias of news articles by using deep learning. It can provide its readers with insights on critical thinking. For this method, we build the dataset for deep learning by analyzing articles' bias from keywords, sourced from the National Assembly proceedings, and assigning bias to said keywords. Based on these data, news article bias is calculated by applying deep learning with a combination of Convolution Neural Network and Recurrent Neural Network. Using this method, 95.6% of sentences are correctly distinguished as either conservative or progressive-biased; on the entire article, the accuracy is 46.0%. This enables analyzing any articles' bias between conservative and progressive unlike previous methods that were limited on article subjects.