• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.3 no.2
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• pp.67-72
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• 2022

Ecological research relies on the interpretation of large amounts of visual data obtained from extensive wildlife surveys, but such large-scale image interpretation is costly and time-consuming. Using an artificial intelligence (AI) machine learning model, especially convolution neural networks (CNN), it is possible to streamline these manual tasks on image information and to protect wildlife and record and predict behavior. Ecological research using deep-learning-based object recognition technology includes various research purposes such as identifying, detecting, and identifying species of wild animals, and identification of the location of poachers in real-time. These advances in the application of AI technology can enable efficient management of endangered wildlife, animal detection in various environments, and real-time analysis of image information collected by unmanned aerial vehicles. Furthermore, the need for school education and social use on biodiversity and environmental issues using AI is raised. School education and citizen science related to ecological activities using AI technology can enhance environmental awareness, and strengthen more knowledge and problem-solving skills in science and research processes. Under these prospects, in this paper, we compare the results of our early 2013 study, which automatically identified African cichlid fish species using photographic data of them, with the results of reanalysis by CNN deep learning method. By using PyTorch and PyTorch Lightning frameworks, we achieve an accuracy of 82.54% and an F1-score of 0.77 with minimal programming and data preprocessing effort. This is a significant improvement over the previous our machine learning methods, which required heavy feature engineering costs and had 78% accuracy.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.37-48
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• 2009

Household service robots should be able to provide their users with a variety of temporal reasoning services. In this paper, we propose an effective way of developing such temporal reasoning services using a domain-independent AI planner. Developing temporal reasoning services with a domain-independent AI planner, we have to address both the knowledge engineering problem of how to represent various real-world temporal constraints in a planning domain definition language, and the system design problem of how to realize the interface between the AI planner and the service consumer. In this paper, we introduce an example scenario and a set of typical temporal constraints for a household service robot, and then present how to represent them in the standard planning domain definition language. We also explain how to implement a service agent based on an AI planner in order to develop and provide new services efficiently.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.240-243
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• 2021

Recently, there have been research results of applying Big data and AI technologies to the evaluation and individual learning for education. It is information technology innovations that collect dynamic and complex data, including student personal records, physiological data, learning logs and activities, learning outcomes and outcomes from social media, MOOCs, intelligent tutoring systems, LMSs, sensors, and mobile devices. In addition, e-learning was generated a large amount of learning data in the COVID-19 environment. It is expected that learning analysis and AI technology will be applied to extract meaningful patterns and discover knowledge from this data. On the learner's perspective, it is necessary to identify student learning and emotional behavior patterns and profiles, improve evaluation and evaluation methods, predict individual student learning outcomes or dropout, and research on adaptive systems for personalized support. This study aims to contribute to research in the field of education by researching and classifying machine learning technologies used in anomaly detection and recommendation systems for educational data.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3D
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• pp.391-397
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• 2009

This research introduces the implementation for cash flow forecasting system in construction company through a case study. The implemented system shows how to develop overall corporate-level and project-level cash flow forecasting model based on a real business process in construction company. It takes 1 year to implement system. The study proposes the way of system design, process of system design, and considerations of implementation in step by step. Moreover, it shows main screen, limitation and reliability of the system. The proposed model is validated accurate, flexible and simple as a result of comparing actual data to forecasting data for 2 years. This system is easy to approach the employee who don't have any financial knowledge. This research is expected to assist to implement system of cash flow forecasting in construction company.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.72-74
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• 2022

The National Science and Technology Knowledge Information Service (NTIS) provides information on national R&D projects. Project information consists of meta-information such as 'project name', 'project performance institution', 'research manager name', and text explaining projects such as 'research goal', 'research content', and 'expected effect'. There is a problem that it takes a lot of time to find the desired project information by checking all of the "research goals" or "research contents" in the list of results of searching for 1 million project information. To solve this problem, this paper proposes a project information summary system that summarizes the parts consisting of long texts within the national R&D project information. By analyzing the linguistic characteristics of the Korean language, a preprocessor was built and a project information summary model based on natural language processing technology was developed to process preprocessed text information. Through this, project information composed of long sentences is provided in a compressed and summarized form, which will help users to easily and quickly infer the overall content with the summary information alone.

• Advances in nano research
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• v.15 no.4
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• pp.355-366
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• 2023

Biological corrosion, a crucial aspect of metal degradation, has received limited attention despite its significance. It involves the deterioration of metals due to corrosion processes influenced by living organisms, including bacteria. Soil represents a substantial threat to pipeline corrosion as it contains chemical and microbial factors that cause severe damage to water, oil, and gas transmission projects. To combat fouling and corrosion, corrosion inhibitors are commonly used; however, their production often involves expensive and hazardous chemicals. Consequently, researchers are exploring natural and eco-friendly alternatives, specifically nano-sized products, as potent corrosion inhibitors. This study aims to environmentally synthesize silver nanoparticles using an extract from Lagoecia cuminoides L and evaluate their effectiveness in preventing biological corrosion of buried pipes in soil. The optimal experimental conditions were determined as follows: a volume of 4 ml for the extract, a volume of 4 ml for silver nitrate (AgNO3), pH 9, a duration of 60 minutes, and a temperature of 60 degrees Celsius. Analysis using transmission electron microscopy confirmed the formation of nanoparticles with an average size of approximately 28 nm, while X-ray diffraction patterns exhibited suitable peak intensities. By employing the Scherer equation, the average particle size was estimated to be around 30 nm. Furthermore, antibacterial studies revealed the potent antibacterial activity of the synthesized silver nanoparticles against both aerobic and anaerobic bacteria. This property effectively mitigates the biological corrosion caused by bacteria in steel pipes buried in soil.

• Journal of Periodontal and Implant Science
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• v.53 no.6
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• pp.444-452
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• 2023

Purpose: The aim of this study was to retrospectively evaluate the survival and failure rates of RESTORE^® implants over a follow-up period of 10-15 years at a university dental hospital and to investigate the factors affecting the survival rate of these dental implants. Methods: A total of 247 RESTORE^® dental implants with a resorbable blast media (RBM) surface inserted in 86 patients between March 2006 and April 2011 at the Department of Periodontology of Seoul National University Dental Hospital were included. Patients with follow-up periods of less than 10 years were excluded, and data analysis was conducted based on dental records and radiographs. Results: Over a 10- to 15-year period, the cumulative survival rate of the implants was 92.5%. Seventeen implants (6.88%) were explanted due to implant fracture (n=10, 4.05%), peri-implantitis (n=6, 2.43%), and screw fracture (n=1, 0.4%). The results of univariate regression analysis using a Cox proportional hazards model demonstrated that implants placed in male patients (hazard ratio [HR], 4.542; 95% confidence interval [CI], 1.305-15.807; P=0.017) and implants that supported removable prostheses (HR, 15.498; 95% CI, 3.105-77.357; P=0.001) showed statistically significant associations with implant failure. Conclusions: Within the limitations of this retrospective study, the RESTORE^® dental implant with an RBM surface has a favorable survival rate with stable clinical outcomes.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.137-152
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• 2012

In the generation of Web 2.0, as many users start to make lots of web contents called user created contents by themselves, the World Wide Web is overflowing by countless information. Therefore, it becomes the key to find out meaningful information among lots of resources. Nowadays, the information retrieval is the most important thing throughout the whole field and several types of search services are developed and widely used in various fields to retrieve information that user really wants. Especially, the legal information search is one of the indispensable services in order to provide people with their convenience through searching the law necessary to their present situation as a channel getting knowledge about it. The Office of Legislation in Korea provides the Korean Law Information portal service to search the law information such as legislation, administrative rule, and judicial precedent from 2009, so people can conveniently find information related to the law. However, this service has limitation because the recent technology for search engine basically returns documents depending on whether the query is included in it or not as a search result. Therefore, it is really difficult to retrieve information related the law for general users who are not familiar with legal terms in the search engine using simple matching of keywords in spite of those kinds of efforts of the Office of Legislation in Korea, because there is a huge divergence between everyday words and legal terms which are especially from Chinese words. Generally, people try to access the law information using everyday words, so they have a difficulty to get the result that they exactly want. In this paper, we propose a term mapping methodology between everyday words and legal terms for general users who don't have sufficient background about legal terms, and we develop a search service that can provide the search results of law information from everyday words. This will be able to search the law information accurately without the knowledge of legal terminology. In other words, our research goal is to make a law information search system that general users are able to retrieval the law information with everyday words. First, this paper takes advantage of tags of internet blogs using the concept for collective intelligence to find out the term mapping relationship between everyday words and legal terms. In order to achieve our goal, we collect tags related to an everyday word from web blog posts. Generally, people add a non-hierarchical keyword or term like a synonym, especially called tag, in order to describe, classify, and manage their posts when they make any post in the internet blog. Second, the collected tags are clustered through the cluster analysis method, K-means. Then, we find a mapping relationship between an everyday word and a legal term using our estimation measure to select the fittest one that can match with an everyday word. Selected legal terms are given the definite relationship, and the relations between everyday words and legal terms are described using SKOS that is an ontology to describe the knowledge related to thesauri, classification schemes, taxonomies, and subject-heading. Thus, based on proposed mapping and searching methodologies, our legal information search system finds out a legal term mapped with user query and retrieves law information using a matched legal term, if users try to retrieve law information using an everyday word. Therefore, from our research, users can get exact results even if they do not have the knowledge related to legal terms. As a result of our research, we expect that general users who don't have professional legal background can conveniently and efficiently retrieve the legal information using everyday words.

• Korean Journal of Construction Engineering and Management
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• v.1 no.3 s.3
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• pp.124-134
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• 2000

In construction projects, the design information, which should contain accurate product information in a systematic way, needs to be applicable through the life-cycle of projects. However, paper-based 2D drawings and relevant documents has difficulties in communicating and sharing the owner's and architect's intention and requirement effectively and building a corporate knowledge base through on-going projects due to Tack of interoperability between specific task or function-oriented software and handling massive information. Meanwhile, computer and information technologies are being developed so rapidly that the practitioners are even hard to adapt them into the industry efficiently. 3D modeling capabilities in CAD systems are enormously developed and enables users to associate 3D models with other relevant information. However, this still requires a great deal of efforts and costs to have all the design information represented in CAD system, and the sophisticated system is difficult to manage. This research focuses on the transition period from 2D-based design Information management to 3D-based, which means co-existence of 2D and 3D-based management. This research proposes a model of a compound system of 2D and 3D-based CAD system which presents the general design information using 3D model integrating with 2D CAD drawings for detailed design information. This research developed an integrated information management system for design and specification by associating 2D drawings and 3D models, where 2D drawings represents detailed design and parts that are hard to express in 3D objects. To do this, related management processes was analyzed to build an information model which in turn became the basis of the integrated information management system.

• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.77-88
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• 2017

Recently, with the advent of knowledge based society where information and knowledge make values, patents which are the representative form of intellectual property have become important, and the number of the patents follows growing trends. Thus, it needs to classify the patents depending on the technological topic of the invention appropriately in order to use a vast amount of the patent information effectively. IPC (International Patent Classification) is widely used for this situation. Researches about IPC automatic classification have been studied using data mining and machine learning algorithms to improve current IPC classification task which categorizes patent documents by hand. However, most of the previous researches have focused on applying various existing machine learning methods to the patent documents rather than considering on the characteristics of the data or the structure of patent documents. In this paper, therefore, we propose to use two structural fields, technical field and background, considered as having impacts on the patent classification, where the two field are selected by applying of the characteristics of patent documents and the role of the structural fields. We also construct multi-label classification model to reflect what a patent document could have multiple IPCs. Furthermore, we propose a method to classify patent documents at the IPC subclass level comprised of 630 categories so that we investigate the possibility of applying the IPC multi-label classification model into the real field. The effect of structural fields of patent documents are examined using 564,793 registered patents in Korea, and 87.2% precision is obtained in the case of using title, abstract, claims, technical field and background. From this sequence, we verify that the technical field and background have an important role in improving the precision of IPC multi-label classification in IPC subclass level.