• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.493-509
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• 2024

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.30-39
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• 2024

This study introduces a novel approach for identifying potential failure risks in missile manufacturing by leveraging Quality Inspection Management (QIM) data to address the challenges presented by a dataset comprising 666 variables and data imbalances. The utilization of the SMOTE for data augmentation and Lasso Regression for dimensionality reduction, followed by the application of a Random Forest model, results in a 99.40% accuracy rate in classifying missiles with a high likelihood of failure. Such measures enable the preemptive identification of missiles at a heightened risk of failure, thereby mitigating the risk of field failures and enhancing missile life. The integration of Lasso Regression and Random Forest is employed to pinpoint critical variables and test items that significantly impact failure, with a particular emphasis on variables related to performance and connection resistance. Moreover, the research highlights the potential for broadening the scope of data-driven decision-making within quality control systems, including the refinement of maintenance strategies and the adjustment of control limits for essential test items.

• Geophysics and Geophysical Exploration
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• v.27 no.3
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• pp.171-180
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• 2024

The processing of seismic data involves analyzing earthquake wave data to understand the internal structure and characteristics of the Earth, which requires high computational power. Recently, machine learning (ML) techniques have been introduced to address these challenges and have been utilized in various tasks such as noise reduction and velocity model construction. However, most studies have focused on specific seismic data processing tasks, limiting the full utilization of similar features and structures inherent in the datasets. In this study, we compared the efficacy of using receiver-wise time-series data ("receiver array") and synchronized receiver signals ("time array") from shotgathers for pretraining a Bidirectional Encoder Representations from Transformers (BERT) model. To this end, shotgather data generated from a synthetic model containing faults was used to perform noise reduction, velocity prediction, and fault detection tasks. In the task of random noise reduction, both the receiver and time arrays showed good performance. However, for tasks requiring the identification of spatial distributions, such as velocity estimation and fault detection, the results from the time array were superior.

• Journal of Korean Society of Forest Science
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• v.103 no.1
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• pp.87-97
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• 2014

This study was conducted to provide field-based harvesting study information which can be used to select an optimal cable system for certain work conditions on steep grounds (> $20^{\circ}$ ground slope) in Korea. To accomplish this study objectives, we evaluated three cable yarding systems (RME-300T tower-yarder, Chuncheon tower-yarder, FARMI tractor winch) working in typical work conditions for their yarding productivity and operational efficiency. Those yarders are commonly used for removing logs or trees on steep grounds in Korea. Under the same work conditions (average DBH of tree to be cut, 20 cm; yarding distance, 60 m; lateral yarding distance, 10 m; and machine utilization rate, 70%), the average productivities were $33.04m^3$/day, $38.47m^3$/day, and $14.17m^3$/day for RME-300T, Chuncheon tower-yarder, and FARMI, respectively. Our standardized cost comparison study also showed that the yarding cost was highest at $37,835won/m^3$ with FARMI, followed by RME-300T at $25,105won/m^3$ for the same work conditions. We found the lowest yarding cost with the Chuncheon tower-yarder at $20,520won/m^3$ which was resulted primarily from high yarding productivity at the yarding distance (60 m). Our analysis suggested that a small machine such as FARMI could be a low-cost yarding machine option for a cable yarding job with a short yarding distance (40 m or less). The Chuncheon tower-yarder is well suited for a mid-range yarding distance job in Korea, ranged between 40 to 140 m. If yarding distance were longer than 140 m, the RME-300T tower yarder appears to be most cost-effective.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.

• Journal of Fashion Business
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• v.13 no.6
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• pp.61-75
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• 2009

The objective of this research is to the enhance the color function of work clothing : to research and analyze the hue and tone of work clothing colors to be used for machinery and heavy industries in national industrial complexes, Through this research, the color using problems which related with safety workers will be revealed. For this project, total 42 sets of work suits were sampled from 12 different companies in the machinery and construction industries in the national industrial complexes of Gyeongsang Namdo Province and 16 sets of work suits currently being sold in the market. The collected work suits samples were classified according to item types and design. Color measurements were taken thus: After calibration according to ASTM D1729 specifications of standardized configuration settings to match standardized luminous source D65(Daylight 6500K) in color cabinet BOTECK SuperLight-VI, the RGB values of the work suits were calculated using PANTONE Color Cue TX. The RGB values of the colors thus derived were converted into V/C values using the Munsell Conversion 9.0.6 and analyzed with Munsell's 10-color system and PCCS. The results were presented according to Munsell's color wheel and color and brightness distributions were expressed in table form, as well as presented as a tone map. Following analysis, color hue distribution was found to be concentrated around PB, and brightness distribution toward the low end and mid range of the scale. Saturation values were distributed mostly around the low end of the scale. Following color tone analysis according to PCCS, it became apparent that colors were mainly distributed around dkg, ltg, and g, at low- and mid-brightness and low-saturation. Therefore, it may be concluded that colors used in work suits in the machinery and heavy industries are mainly cool colors, at low- and mid-brightness and low saturation. It is conjectured that such colors were applied uniformly in the workplace in order to serve certain functions, such as concealment of stains and contamination. Therefore, it follows that the utilization of colors, among other functions served by working clothings, must be taken into consideration in order to enhance safety and efficiency.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.7
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• pp.3562-3569
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• 2013

The performance of concrete structure decreases with change in time and the external environment. In order to reinforce the structure, the research about new material development and application of newly developed materials are widely conducted. In the case of composite FRP, it received good attention in the academia due to its high intensity-weight ratio, excellent corrosion resistency as well as good workability. When applying at the construction field, however, the utilization of FRP did not increase as much due to lack of reliability and design standard. Current study investigated the material characteristics during the temperature change at high temperature and the structural behavior from restraint effect for GFRP reinforcing materials. Two experimental variables were set in this study: GFRP reinforcements due to tensile properties of temperature and restraint compression effects. Three concrete specimen were selected for each set temperatures. For this reason, as a variable to experiment with the effects confined compression concrete members value and tensile properties with temperature reinforcement GFRP, experiment produced three pieces each for each set temperature, the concrete specimen, which is confined in the GFRP was selected each I did. For the temperature change during the experiment, the concrete specimen were mounted in order to expose to experimental high temperature for certain period of time. For compression performance evaluation, reinforcement effect from horizontal constraint of the fiber were measured using an Universal Material Testing Machine (UTM). Finally, this study revealed that the binding characteristics of GFRP materials from temperature change decreased. Also, this study showed that the maximum compression intensity decreased as the temperature increased up to $150^{\circ}C$ in the constraints ability of the GFRP reinforcements during the horizontal constraint of concrete.

• Journal of the Korea Society for Simulation
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• v.17 no.2
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• pp.63-74
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• 2008

The design of best efficient production process is common requirements of the production strategy department and the process planning department to maximize the revenue and accomplish target production volumes in the production periods. And they use several general methods for that-line-balancing, removing of the bottle-neck process, facility ramp-up, increasing of the worker's utilization, etc. But, those methods have depended on analytic, static and arithmetic calculations, yet. So, irregular work-waiting time causing the delay time isn't include in extracting production capacity, especially in the line production process. The work-waiting time is changed irregularly along the variation of each machine and very important for calculate real product lead-time and forecasting target production volumes. At this thesis, i'm going to mention the importance of the delay time of conveyor system which can be extracted by discrete-event simulation. And suggest it as a new main variable that must be considered at designing new production system. Then experimented and tested that's effects in the H-company case, conveyor based line production process.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.414-419
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• 2020

An artificial intelligence-based cable tension estimation model was developed to expand the utilization of data obtained from cable accelerometers of cable-stayed bridges. The model was based on an algorithm for selecting the natural frequency in the tension estimation process based on the vibration method and an applied artificial neural network (ANN). The training data of the ANN was composed after converting the cable acceleration data into the frequency, and machine learning was carried out using the characteristics with a pattern on the natural frequency. When developing the training data, the frequencies with various amplitudes can be used to represent the frequencies of multiple shapes to improve the selection performance for natural frequencies. The performance of the model was estimated by comparing it with the control criteria of the tension estimated by an expert. As a result of the verification using 139 frequencies obtained from the cable accelerometer as the input, the natural frequency was determined to be similar to the real criteria and the estimated tension of the cable by the natural frequency was 96.4% of the criteria.

• Journal of the Korean Wood Science and Technology
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• v.35 no.6
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• pp.1-12
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• 2007

For the purpose of effective utilization of domestic second-grown larch as structural members, post and beam construction applying traditional construction to Japanese larch glulam members was adopted with processing by machine pre-cut method. In general, horizontal shear test by KS F 2154 is conducted to assess the horizontal shear properties of the wooden structure by post and beam construction. The frame was consisted of post and beam member with appropriate fasteners, and members have their own processed parts (notch, hole, etc.) that can be well-connected each other. The shear wall was consisted of the frame with screw-nail sheathed panel (OSB). The results of horizontal shear loading tests without vertical loads conducted on the frame and the shear wall structures, the maximum strengths were about 1.9 kN/m and about 9.7 kN/m, the shear rigidities were about 167 kN/rad, 8198 kN/rad, respectively. The strength proportion of the frame specimen was about 20% of the wall's and about 2% in initial stiffness. Nail failures are remarkable on the shear wall specimen with punching shears and shear failures. The shear load factor for the shear wall specimen by the method of Architectural Institute of Japan was 1.5, which was obtained by the bi-linear method. Loading method should be considered to obtain smooth load-deformation relationship. For the better shear performance of the structures, column base and post and beam connections and sheathed panel should be further examined as well.