• Journal of Korea Society of Industrial Information Systems
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• v.28 no.4
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• pp.11-19
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• 2023

The paper proposes a smart mirror system that recommends fragrances based on user emotion analysis. This paper combines natural language processing techniques such as embedding techniques (CounterVectorizer and TF-IDF) and machine learning classification models (DecisionTree, SVM, RandomForest, SGD Classifier) to build a model and compares the results. After the comparison, the paper constructs a personal emotion-based fragrance recommendation mirror model based on the SVM and word embedding pipeline-based emotion classifier model with the highest performance. The proposed system implements a personalized fragrance recommendation mirror based on emotion analysis, providing web services using the Flask web framework. This paper uses the Google Speech Cloud API to recognize users' voices and use speech-to-text (STT) to convert voice-transcribed text data. The proposed system provides users with information about weather, humidity, location, quotes, time, and schedule management.

• Journal of the Korean Chemical Society
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• v.67 no.5
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• pp.362-377
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• 2023

The purpose of this study was to investigate the effect of semantic mapping as a science text reading strategy on high school students' inferential understanding. For this purpose, eight science text reading classes were conducted a reading strategy using semantic mapping for 46 students in two science-focused classes in the third grade of a high school. To investigate the effects of semantic mapping reading strategy on students' inferential comprehension, students' pre- and post-reading ability tests results were analyzed. In order to find out the change in inferential comprehension, the level of the inferential comprehension was analyzed using the analysis framework for developed in this study. For the classification of inferential comprehension, the levels of the inferential comprehension were converted into scores. The results of the analysis of changes in students' inferential comprehension showed that semantic mapping reading strategy classes influenced the changes in high school students' inference, especially bridge inference and elaborative inference among sub-elements of inferential comprehension.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• Nuclear Engineering and Technology
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• v.54 no.12
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• pp.4809-4818
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• 2022

The decommissioning project of a nuclear facility is a large-scale process that is expected to take about 15 years or longer. The range of risks to be considered is large and complex, then, it is expected that various risks will arise in decision-making by area during the project. Therefore, in this study, the risk family derived from the Decommissioning Risk Management (DRiMa) project was reconstructed into a decommissioning project risk profile suitable for the Kori Unit 1. Two criteria of uncertainty and importance are considered in order to prioritize the selected 26 risks of decommissioning project. The uncertainty is scored according to the relevant laws and decommissioning plan preparation guidelines, and the project importance is scored according to the degree to which it primarily affects the triple constraints of the project. The results of risks are divided into high, medium, and low. Among them, 10 risks are identified as medium level and 16 risks are identified as low level. 10 risks, which are medium levels, are classified in five categories: End state of decommissioning project, Management of waste and materials, Decommissioning strategy and technology, Legal and regulatory framework, and Safety. This study is a preliminary assessment of the risk of the decommissioning project that could be considered in the preparation stage. Therefore, we expect that the project risks considered in this study can be used as an initial data for reevaluation by reflecting the detail project progress in future studies.

• International conference on construction engineering and project management
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• 2020.12a
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• pp.157-166
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• 2020

Automated rebar fabrication, which requires effective information exchange between model designers and fabricators, has brought the integration and interoperability of data from different sources to the notice of both academics and industry practitioners. Industry Foundation Classes (IFC) was one of the most commonly used data formats to represent the semantic information of prefabricated components in buildings, whereas the data format utilized by rebar fabrication machine is BundesVereinigung der Bausoftware (BVBS), which is a numerical data structure exchanging reinforcement information through ASCII encoded files. Seamless transformation between IFC and BVBS empowers the automated rebar fabrication and improve the construction productivity. In order to improve data interoperability between IFC and BVBS, this study presents an IFC extension based on the attributes required by automated rebar fabrication machines with the help of Information Delivery Manual (IDM) and Model View Definition (MVD). IDM is applied to describe and display the information needed for the design, construction and operation of projects, whereas MVD is a subset of IFC schema used to describe the automated rebar fabrication workflow. Firstly, with a rich pool of vocabularies practitioners, OmniClass is used in information exchange between IFC and BVBS, providing a hierarchy classification structure for reinforcing elements. Then, using International Framework for Dictionaries (IFD), the usage of each attribute is defined in a more consistent manner to assist the data mapping process. Besides, in order to address missing information within automated fabrication process, a schematic data mapping diagram has been made to deliver IFC information from BIM models to BVBS format for better data interoperability among different software agents. A case study based on the data mapping will be presented to demonstrate the proposed IFC extension and how it could assist/facilitate the information management.

• Journal of Biomedical Engineering Research
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• v.44 no.6
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• pp.414-427
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• 2023

Implantable bioabsorbable combination products undergo inherent degradation and systemic absorption within the physiological environment, thereby streamlining the therapeutic regimen and obviating the imperative for invasive extraction procedures. This inherent property not only enhances patient convenience and therapeutic efficacy but also underpins a paradigm of support characterized by heightened safety parameters. Within the regulatory landscapes of Korea, the United States, and Europe, implantable bioabsorbable combination products are meticulously classified into distinct categories, either as pharmaceutical implants or as implantable medical devices, depending on their primary mode of action. This scholarly investigation systematically examines the regulatory frameworks governing implantable bioabsorbable combination products in South Korea, the United States, and Europe. Notable discrepancies across national jurisdictions emerge concerning regulatory specifics, including terminology, product classification, and product name associated with these products. The conspicuous absence of standardized approval regulations presents a formidable barrier to the commercialization of these advanced medical devices. This academic discourse passionately emphasizes the critical need for formulating and implementing a sophisticated regulatory framework capable of streamlining the product approval process, thereby paving the way for a seamless path to commercializing implantable bioabsorbable combination products.

• Journal of Korean Neurosurgical Society
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• v.66 no.6
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• pp.632-641
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• 2023

Central nervous system tumors are identified as tumors of the brain and spinal cord. The associated morbidity and mortality of cerebrospinal tumors are disproportionately high compared to other malignancies. While minimally invasive techniques have initiated a revolution in neurosurgery, artificial intelligence (AI) is expediting it. Our study aims to analyze AI's role in the neurosurgical management of cerebrospinal tumors. We conducted a scoping review using the Arksey and O'Malley framework. Upon screening, data extraction and analysis were focused on exploring all potential implications of AI, classification of these implications in the management of cerebrospinal tumors. AI has enhanced the precision of diagnosis of these tumors, enables surgeons to excise the tumor margins completely, thereby reducing the risk of recurrence, and helps to make a more accurate prediction of the patient's prognosis than the conventional methods. AI also offers real-time training to neurosurgeons using virtual and 3D simulation, thereby increasing their confidence and skills during procedures. In addition, robotics is integrated into neurosurgery and identified to increase patient outcomes by making surgery less invasive. AI, including machine learning, is rigorously considered for its applications in the neurosurgical management of cerebrospinal tumors. This field requires further research focused on areas clinically essential in improving the outcome that is also economically feasible for clinical use. The authors suggest that data analysts and neurosurgeons collaborate to explore the full potential of AI.

• Journal of the Korea Institute of Building Construction
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• v.23 no.5
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• pp.583-593
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• 2023

For a holistic and precise assessment of safety benchmarks within a construction venture, it's paramount to delineate between the intrinsic features of the construction and its real-time, on-site performance metrics. In this study, we delved into genuine accident instances to discern the interplay between these construction attributes and on-ground performance determinants in relation to safety mishaps, employing the binomial logit analytical framework. Our scrutiny underscored that construction expenditure profoundly modulates the likelihood of fatal occurrences. Notably, variables pertinent to on-site safety protocols wielded considerable influence over both fatal mishaps and accidents implicating multiple personnel. These revelations intimate that while ascertaining the safety quotient of a construction initiative, a mere classification and recalibration based on fiscal dimensions can elucidate much. Yet, a comprehensive safety appraisal necessitates transcending quantitative indices, such as frequency of mishaps or casualty rates, to encapsulate the multifaceted interventions and strategies adopted at the construction locale.

• Convergence Security Journal
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• v.24 no.2
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• pp.19-29
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• 2024

A crypto-jacking attack is an attack that infringes on the availability of users by stealing computing resources required for cryptocurrency mining. The target of the attack is gradually diversifying from general desktop or server environments to cloud environments. Therefore, it is essential to apply a crypto-minor detection technique suitable for various computing environments. However, since the existing detection methodologies have only been detected in a specific environment, comparative analysis has not been properly performed on the methodologies that can be applied to each environment. Therefore, in this study, classification criteria for conventional crypto-minor detection techniques are established, and a complex and integrated detection framework applicable to the cloud environment is presented through in-depth comparative analysis of existing crypto-minor detection techniques based on different experimental environments and datasets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.6
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• pp.1478-1499
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• 2024

Coronavirus disease (COVID-19) is an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 become an active epidemic disease due to its spread around the globe. The main causes of the spread are through interaction and transmission of the droplets through coughing and sneezing. The spread can be minimized by isolating the susceptible patients. However, it necessitates remote monitoring to check the breathing issues of the patient remotely to minimize the interactions for spread minimization. Thus, in this article, we offer a wearable-IoTs-centered framework for remote monitoring and recognition of the breathing pattern and abnormal breath detection for timely providing the proper oxygen level required. We propose wearable sensors accelerometer and gyroscope-based breathing time-series data acquisition, temporal features extraction, and machine learning algorithms for pattern detection and abnormality identification. The sensors provide the data through Bluetooth and receive it at the server for further processing and recognition. We collect the six breathing patterns from the twenty subjects and each pattern is recorded for about five minutes. We match prediction accuracies of all machine learning models under study (i.e. Random forest, Gradient boosting tree, Decision tree, and K-nearest neighbor. Our results show that normal breathing and Bradypnea are the most correctly recognized breathing patterns. However, in some cases, algorithm recognizes kussmaul well also. Collectively, the classification outcomes of Random Forest and Gradient Boost Trees are better than the other two algorithms.