• Advances in concrete construction
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• v.16 no.2
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• pp.97-106
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• 2023

This research endeavors to explore the enhancement of workforce economic efficiency through the application of nanotechnology, focusing on its economic implications. The findings of this investigation reveal that in recent years, surging global population growth and escalating demands for products and services have led to excessive resource consumption, resulting in adverse environmental consequences and altering environmental conditions-a phenomenon referred to as the economic growth dilemma. Entrepreneurs and economic stakeholders have begun to recognize the importance of sustainable development and the integration of environmental considerations into the production of goods and services. Within this context, knowledge-based economies have emerged as a driving force for sustainable business practices, particularly in the realm of nanotechnology. The integration of nanotechnology across various industries, including pharmaceuticals, agriculture, environmental management, and the chemical and petroleum sectors, as well as energy distribution, has yielded remarkable results. Consequently, this research aims to investigate the application and integration of nanotechnology in environmentally friendly silver nanoparticle production within select industries. Subsequently, it will examine the far-reaching implications of nanotechnology on economic growth and sustainable development.

• Journal of Information Technology Services
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• v.15 no.2
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• pp.65-80
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• 2016

Currently, many universities offer a variety of programs for students to improve their knowledge and expertise for their career development. Because each program's success depends on students' active participation and passion, the university makes a lot of efforts to motivate them enhance the loyalty toward the school. Several universities in Korea operate their own loyalty program based on their students' activities. However, we need to develop a distinctive loyalty program to suit universities' education environment because the purpose of education is different from existing commercial purpose. This study shows the process that improves problems of loyalty programs that are operated by the K University and suggests new ideas based on design thinking methods. Also, this study includes a process that changes standardized and involuntary loyalty program to interesting loyalty program that induces students' voluntary participation through combining with gamification concept. The method that we suggest in this study is expected to extend various fields.

• The Korean Journal of Emergency Medical Services
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• v.20 no.3
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• pp.69-83
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• 2016

Purpose: The purpose of this study was to explore the factors influencing evidence-based fall prevention nursing performance of hospital nurses. Methods: A self-reported questionnaire was completed by 344 nurses from three general hospitals from January 20 to March 10, 2013. The study instruments included general characteristics of the subjects, and awareness and performance of fall prevention. Data were analyzed by t test, ANOVA, Pearson's correlation, and multiple regression using SPSS v. 20.0. Results: There were statistically significant differences in awareness and performance according to age, marital status, clinical experiences, workplace, experience of fall prevention education, knowledge of fall prevention, compliance with fall prevention, attention level toward prevention, recognition level of potential falls, nurse responsibility for falls, importance of fall prevention, efforts level for fall prevention, and awareness score of falls prevention. There was a positive correlation among awareness and performance of fall prevention. Based on the multiple regression analysis, compliance with fall prevention, efforts level for fall prevention, and awareness score of falls prevention were significant predictors for performance of fall prevention. The explanation power of the model was 64.1%. Conclusion: The findings revealed the need to develop an effective nursing intervention to improve hospital nurses' performance for fall prevention.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.85-95
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• 2016

Recently, deep learning and machine learning have attracted considerable attention and many supporting frameworks appeared. In artificial intelligence field, a large body of research is underway to apply the relevant knowledge for complex problem-solving, necessitating the application of various learning algorithms and training methods to artificial intelligence systems. In addition, there is a dearth of performance evaluation of decision making agents. The decision making agent that can find optimal solutions by using reinforcement learning methods designed through this research can collect raw pixel data observed from dynamic environments and make decisions by itself based on the data. The decision making agent uses convolutional neural networks to classify situations it confronts, and the data observed from the environment undergoes preprocessing before being used. This research represents how the convolutional neural networks and the decision making agent are configured, analyzes learning performance through a value-based algorithm and a policy-based algorithm : a Deep Q-Networks and a Policy Gradient, sets forth their differences and demonstrates how the convolutional neural networks affect entire learning performance when using pixel data. This research is expected to contribute to the improvement of artificial intelligence systems which can efficiently find optimal solutions by using features extracted from raw pixel data.

• Proceedings of the Korea Contents Association Conference
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• 2008.05a
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• pp.851-854
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• 2008

Science-based Industry which is future growth industry leading national competitiveness in knowledge-based society is highly science-dependent field when developing technologies. And highly science-based field is absolutely dependent on core scholarly papers. Due to researchers' need of academic papers related to developing technologies and advances of linking technologies, it is possible to link service between patents and scholarly papers. This paper's purpose is to describe on implementation linking service between EPO patent and papers that cited on search reports. First, it describe case study of other linking services. Second, it describes a kinds of data used in linking services. Lastly, it describe implementation of linking different kinds of contents (patents and papers) in KISTI.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.244-251
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• 2007

Intention reading technique is essential to provide personalized services toward more convenient and human-friendly services in complex ubiquitous environment such as a smart home. If a system has knowledge about an user's intention of his/her behavioral pattern, the system can provide mote qualified and satisfactory services automatically in advance to the user's explicit command. In this sense, learning capability is considered as a key function for the intention reading technique in view of knowledge discovery. In this paper, ore introduce a personalized media control method for a possible application iii a smart home. Note that data pattern such as human behavior contains lots of inconsistent data due to limitation of feature extraction and insufficiently available features, where separable data groups are intermingled with inseparable data groups. To deal with such a data pattern, we introduce an effective engineering approach with the combination of fuzzy logic and probabilistic reasoning. The proposed learning system, which is based on IFCS (Iterative Fuzzy Clustering with Supervision) algorithm, extract probabilistic fuzzy rules effectively from the given numerical training data pattern. Furthermore, an extended architectural design methodology of the learning system incorporating with the IFCS algorithm are introduced. Finally, experimental results of the media contents recommendation system are given to show the effectiveness of the proposed system.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.25 no.3
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• pp.165-189
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• 2014

This study examined the current practices of Research Data Management (RDM) services recently built and implemented at research university libraries in the U.S. by analyzing the components of the services and the contents presented in their web sites. The study then analyzed the content of web pages describing the services provided by 31 Research Universities/Very High research activity determined based on the Carnegie Classification. The analysis was based on 9 components of the services suggested by previous studies, including (1) DMP support; (2) File organization; (3) Data description; (4) Data storage; (5) Data sharing and access; (6) Data preservation; (7) Data citation; (8) Data management training; (9) Intellectual property of data. As a result, the vast majority of the universities offered the service of DMP support. More than half of the universities provided the services for describing and preserving data, as well as data management training. Specifically, RDM services focused on offering the guidance to disciplinary metadata and repositories of relevance, or training via individual consulting services. More research and discussion is necessary to better understand an intra- or inter-institutional collaboration for implementing the services and knowledge and competency of librarians in charge of the services.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.608-615
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• 2008

In Knowledge Q&A services where information is created by unspecified users, document quality is an important factor of user satisfaction with search results. Previous work on quality prediction of Knowledge Q&A documents evaluate the quality of documents by using non-textual information, such as click counts and recommendation counts, and focus on enhancing retrieval performance by incorporating the quality measure into retrieval model. Although the non-textual information used in previous work was proven to be useful by experiments, data sparseness problem may occur when predicting the quality of newly created documents with such information. To solve data sparseness problem of non-textual features, this paper proposes new features for document quality prediction, namely text-confidence features, which indicate how trustworthy the content of a document is. The proposed features, extracted directly from the document content, are stable against data sparseness problem, compared to non-textual features that indirectly require participation of service users in order to be collected. Experiments conducted on real world Knowledge Q&A documents suggests that text-confidence features show performance comparable to the non-textual features. We believe the proposed features can be utilized as effective features for document quality prediction and improve the performance of Knowledge Q&A services in the future.

• Journal of the Korean Society for information Management
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• v.21 no.3
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• pp.251-267
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• 2004

The purpose of this study is to generate the local level knowledge structure of a single document, similar to end-of-the-book indexes and table of contents of printed material through the use of term clustering and cluster representative term selection. Furthermore, it aims to analyze the functionalities of the knowledge structure. and to confirm the applicability of these methods in user-friend1y information services. The results of the term clustering experiment showed that the performance of the Ward's method was superior to that of the fuzzy K -means clustering method. In the cluster representative term selection experiment, using the highest passage frequency term as the representative yielded the best performance. Finally, the result of user task-based functionality tests illustrate that the automatically generated knowledge structure in this study functions similarly to the local level knowledge structure presented In printed material.