The Bayesian network (BN) model was applied to analyze the characteristic variables that affect compliance with safety inspections of farmed eel during the production stage, using the data from 30,063 cases of eel aquafarm safety inspection in the Integrated Food Safety Information Network (IFSIN) from 2012 to 2021. The dataset for establishing the BN model included 77 non-conforming cases. Relevant HACCP data, geographic information about the aquafarms, and environmental data were collected and mapped to the IFSIN data to derive explanatory variables for nonconformity. Aquafarm HACCP certification, detection history of harmful substances during the last 5 y, history of nonconformity during the last 5 y, and the suitability of the aquatic environment as determined by the levels of total coliform bacteria and total organic carbon were selected as the explanatory variables. The highest achievable eel aquafarm noncompliance rate by manipulating the derived explanatory variables was 24.5%, which was 94 times higher than the overall farmed eel noncompliance rate reported in IFSIN between 2017 and 2021. The established BN model was validated using the IFSIN eel aquafarm inspection results conducted between January and August 2022. The noncompliance rate in the validation set was 0.22% (15 nonconformances out of 6,785 cases). The precision of BN model prediction was 0.1579, which was 71.4 times higher than the non-compliance rate of the validation set.
This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.
With the development of artificial intelligence technology, interest in data-based product preference estimation and personalized recommender systems is increasing. However, if the recommendation is not suitable, there is a risk that it may reduce the purchase intention of the customer and even extend to a huge financial loss due to the characteristics of the financial product. Therefore, developing a recommender system that comprehensively reflects customer characteristics and product preferences is very important for business performance creation and response to compliance issues. In the case of financial products, product preference is clearly divided according to individual investment propensity and risk aversion, so it is necessary to provide customized recommendation service by utilizing accumulated customer data. In addition to using these customer behavioral characteristics and transaction history data, we intend to solve the cold-start problem of the recommender system, including customer demographic information, asset information, and stock holding information. Therefore, this study found that the model proposed deep learning-based collaborative filtering by deriving customer latent preferences through characteristic information such as customer investment propensity, transaction history, and financial product information based on customer transaction log records was the best. Based on the customer's financial investment mechanism, this study is meaningful in developing a service that recommends a high-priority group by establishing a recommendation model that derives expected preferences for untraded financial products through financial product transaction data.
Journal of the Korea Institute of Building Construction
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v.24
no.1
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pp.157-168
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2024
The frequent occurrence of structural failures at building construction sites in Korea has underscored the critical role of rigorous oversight in the inspection and management of construction projects. As mandated by prevailing regulations and standards, onsite supervision by designated supervisors encompasses thorough documentation of construction quality, material standards, and the history of any reconstructions, among other factors. These reports, predominantly consisting of unstructured data, constitute approximately 80% of the data amassed at construction sites and serve as a comprehensive repository of quality-related information. This research introduces the SL-QPA model, which employs text mining techniques to preprocess supervision reports and establish a sentiment dictionary, thereby enabling the quantification of quality performance. The study's findings, demonstrating a statistically significant Pearson correlation between the quality performance scores derived from the SL-QPA model and various legally defined indicators, were substantiated through a one-way analysis of variance of the correlation coefficients. The SL-QPA model, as developed in this study, offers a supplementary approach to evaluating the quality performance of building construction projects. It holds the promise of enhancing quality inspection and management practices by harnessing the wealth of unstructured data generated throughout the lifecycle of construction projects.
The purpose of this paper is to restore the academic status of Gungwi perception a little. The symbolism of Gungwi, or Year Month Day Hour, likened to Geun Myo Hwa Sil, is not just a technique of interpretation. Recognizing that it corresponds to Saju's most fundamental Mingli principle, the study was conducted to the effect that more academic research should be conducted in the future. The intrinsic idea that constitutes Saju is the yin-yang and the five elements, the letters recorded are twelve-dimensional, and the elements in charge of the space and time are Cheongan, Jeeji, and Gungwi, which are woven into four pillars. Through this consideration of Gungwi's perception, we presented the "spectrum of time" phenomenon that past time and information pass through the point of time, spread like a spectrum, and lead future time and action at the time when humans are born, that is, the energy of the universe is formatted throughout the brain and body. We discussed the change point of Eight Trigrams used by Lim Cheol Cho as a basis for explaining 'Won Hyong I Jeong' and the assumption that the time change or distortion of the two cones penetrating the present, which is assumed in parallel theory, one of the modern cosmologies, leaves an afterimage in the future universe as Gungwi's deductive basis.
In modern society, crime records have been digitized. Digital information is difficult to distinguish from original information, but the former is easy to modulate. This situation explains the increasing importance of digital forensics. However, digital forensic has several inefficiencies because of the rapid development of technology, unclear jurisdiction, and tool errors. This study surveyed digital forensic specialists and derived the priority of domestic digital forensic issues by redefining 17 issues in digital forensics from Brungs-Jamieson study in Australia. The present study was divided into four groups, namely, police, government and public corporations, private companies, and legal groups. The study could compare and analyze comparative analysis of existing studies in Australia and the US. This study can also examine differences in the results of each group in Korea. Thus, the key issues in Korea were derived as "Requirements to 'Fire Up' Original." The differences of the three groups in terms of legal issues were then identified. This finding enables us to understand differences in priorities and importance between groups and countries.
Since the end of the Cold War in the 1990s, European countries have cut defense costs and reduced armaments as an era of peace without large-scale wars continues, and as a result, the West's defense industry base has gradually weakened. On the other hand, South Korea, the world's only divided country, was able to achieve high growth in the defense industry as a result of continuous arms strengthening in the face of North Korea's nuclear and missile threats. With the rapid increase in demand for conventional weapons systems and changes in the structure of the global defense market due to the Russia-Ukraine war, Korea's weapons system drew great attention as a large-scale defense export contract with Poland was signed in 2022. In 2023, K-Defense ranked ninth in the world's arms exports and aims to become the world's fourth-largest defense exporter by 2027. Therefore, this study analyzed the case of Korea-Poland defense exports to derive problems, and presented development strategies related to export revitalization of K-Defense, a national strategic industry. In order for the defense industry to become Korea's next growth engine, it is necessary to establish a defense organization, prepare government-level measures to protect defense industry technology, and expand military and security cooperation with allies linked to defense exports.
Park, Jimin;Seo, Wanhyuk;Seo, Dong-Hee;Yun, Tae-Sup
Journal of the Korean Geotechnical Society
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v.40
no.4
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pp.69-79
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2024
Field geotechnical data are obtained from various field and laboratory tests and are documented in geotechnical investigation reports. For efficient design and construction, digitizing these geotechnical parameters is essential. However, current practices involve manual data entry, which is time-consuming, labor-intensive, and prone to errors. Thus, this study proposes an automatic data extraction method from geotechnical investigation reports using image-based deep learning models and text-mining techniques. A deep-learning-based page classification model and a text-searching algorithm were employed to classify geotechnical investigation report pages with 100% accuracy. Computer vision algorithms were utilized to identify valid data regions within report pages, and text analysis was used to match and extract the corresponding geotechnical data. The proposed model was validated using a dataset of 205 geotechnical investigation reports, achieving an average data extraction accuracy of 93.0%. Finally, a user-interface-based program was developed to enhance the practical application of the extraction model. It allowed users to upload PDF files of geotechnical investigation reports, automatically analyze these reports, and extract and edit data. This approach is expected to improve the efficiency and accuracy of digitizing geotechnical investigation reports and building geotechnical databases.
Jun, Seong Joon;Hong, Sang Bum;Hur, Soon Do;Lee, Jeonghoon;Kang, Jung-Ho;Hwang, Hee Jin;Chung, Ji Woong;Jung, Hye Jin;Han, Changhee;Hong, Sungmin
Ocean and Polar Research
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v.36
no.1
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pp.13-24
/
2014
We established the first complete ice core processing method and analytical procedures for fundamental proxies, using a 40.2 m long ice core drilled on the Mt. Tsambagarav glacier in the Mongolian Altai mountains in July 2008. The whole core was first divided into two sub ice core sections and the measurements of the visual stratigraphy and electrical conductivity were performed on the surface of these sub core sections. A continuous sequence of samples was then prepared for chemical analyses (stable isotope ratios of oxygen ($^{18}O/^{16}O$) and hydrogen ($^2H/^1H$), soluble ions and trace elements). A total of 29 insoluble dust layers were identified from the measurement of visual stratigraphy. The electrical conductivity measurement (ECM) shows 11 peaks with the current more than 0.8 ${\mu}A$ Comparing the profiles of $SO_4{^{2-}}$ and $Cl^-$ concentrations to correlate with known volcanic eruptions, the first two ECM peaks appear to be linked to the eruptions (January and June 2007) of Kliuchevskoi volcano on the Kamchatka Peninsula of Russia, which supports the reliability of our ECM data. Finally, the composition of stable isotopes (${\delta}^{18}O$ and ${\delta}D$) shows a well-defined seasonal variation, suggesting that various chemical proxies may have been well preserved in the successive ice layers of Tsambagarav ice core. Our ice core processing method and analytical procedures for fundamental proxies are expected to be used for paleoclimate and paleoenvironmental studies from polar and alpine ice cores.
The image quality management of bone mineral density is the responsibility and duty of radiologists who carry out examinations. However, inaccurate conclusions due to lack of understanding and ignorance regarding the methodology of image quality management can be a fatal error to the patient. Therefore, objective of this paper is to understand proper image quality management and enumerate methods for examiners and patients, thereby ensuring the reliability of bone mineral density exams. The accuracy and precision of bone mineral density measurements must be at the highest level so that actual biological changes can be detected with even slight changes in bone mineral density. Accuracy and precision should be continuously preserved for image quality of machines. Those factors will contribute to ensure the reliability in bone mineral density exams. Proper equipment management or control methods are set with correcting equipment each morning and after image quality management, a phantom, recommended from the manufacturer, is used for ten to twenty-five measurements in search of a mean value with a permissible range of ${\pm}1.5%$ set as standard. There needs to be daily measurement inspections on the phantom or at least inspections three times a week in order to confirm the existence or nonexistence of changes in values in actual bone mineral density. in addition, bone mineral density measurements were evaluated and recorded following the rules of Shewhart control chart. This type of management has to be conducted for the installation and movement of equipment. For the management methods of inspectors, evaluation of the measurement precision was conducted by testing the reproducibility of the exact same figures without any real biological changes occurring during reinspection. Bone mineral density inspection was applied as the measurement method for patients either taking two measurements thirty times or three measurements fifteen times. An important point when taking measurements was after a measurement whether it was the second or third examination, it was required to descend from the table and then reascend. With a 95% confidence level, the precision error produced from the measurement bone mineral figures came to 2.77 times the minimum of the biological bone mineral density change. The value produced can be stated as the least significant change (LSC) and in the case the value is greater, it can be stated as a section of genuine biological change. From the initial inspection to equipment moving and shifter, management must be carried out and continued in order to achieve the effects. The enforcement of proper quality control of radiologists performing bone mineral density inspections which brings about the durability extensions of equipment and accurate results of calculations will help the assurance of reliable inspections.
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