• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.23-31
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• 2023

Research and interest in sustainable printing are increasing in the packaging printing industry. Currently, predicting the amount of ink required for each work is based on the experience and intuition of field workers. Suppose the amount of ink produced is more than necessary. In this case, the rest of the ink cannot be reused and is discarded, adversely affecting the company's productivity and environment. Nowadays, machine learning models can be used to figure out this problem. This study compares the ink usage prediction machine learning models. A simple linear regression model, Multiple Regression Analysis, cannot reflect the nonlinear relationship between the variables required for packaging printing, so there is a limit to accurately predicting the amount of ink needed. This study has established various prediction models which are based on CART (Classification and Regression Tree), such as Decision Tree, Random Forest, Gradient Boosting Machine, and XGBoost. The accuracy of the models is determined by the K-fold cross-validation. Error metrics such as root mean squared error, mean absolute error, and R-squared are employed to evaluate estimation models' correctness. Among these models, XGBoost model has the highest prediction accuracy and can reduce 2134 (g) of wasted ink for each work. Thus, this study motivates machine learning's potential to help advance productivity and protect the environment.

• Asia Marketing Journal
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• v.16 no.1
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• pp.215-228
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• 2014

The automotive industry plays a significant role in the global economy. One of the reasons is that this industry compasses every aspects of the value chain - from raw materials to design and development, manufacturing, sales and services, and even disposal. Thus, the industry needs significant upfront capital investment and requires years of R&D and market development. As a result, this industry is dominated by a handful of global players and it is not easy for a new entrant to enter this industry. Furthermore, success is even more difficult to achieve. How did Hyundai Motor make it in this tough marketplace? Can it continue against all odds? The CAGR for last 5 years is 12% and it stands at 6th in the world. Compared to other global brands, Hyundai has geographically well-balanced sales portfolio. The quality improvement is outstanding. The brand performance follows these quality and sales improvements. Yet, the global competition is ever intensifying. Now, it is the time to step up once more. The next strategic goal needs fundamental shift toward brand and marketing-focus. In constructing global marketing strategy, Hyundai Motor's vision is "Lifetime partner in mobility and beyond" and its goal is global top 3 brand by year 2015 through modern premium brand image and selling 5 million vehicles. The target brand positioning of Hyundai Motor is the leading position in premium dimension and stylish/modern dimension. The global brand strategy framework is based on the brand direction of "Modern Premium" and is designed to deliver core brand identity (i.e., Simple, Creative, Caring) to customers. In order to manage brand performance, Hyundai's marketing platformalso includes marketing performance management, brand performance management, and market driven organization. From this diagnosis, Hyundai Motor is well posed to build a strong brand. Nevertheless, there are still challenges ahead from consumer, technology, competitor, and macro-environment perspectives. To overcome these threats, the bases of competition for all successful automotive brands are various differentiation factors, including technology, performance, value proposition, or heritage. Hyundai Motor is well prepared so far. However, it is not tested against time yet whether Hyundai can overcome these unforeseeable major threats. Hyundai is trying to find the solution from a strong brand, while believing in "New Thinking. New Possibilities."

• Analytical Science and Technology
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• v.36 no.5
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• pp.216-223
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• 2023

Aluminium (Al) is one of the major elements in rocks and its concentration can be varied, depending on different rock types as well as sources. The present study aimed to propose an analytical method based on the UV-Vis as a cheap, simple, and common instrument equipped in most laboratories for Al quantification in rocks after the microwave assisted acid digestion. The aluminone and 8-hydroxyquinoline were investigated for the colorimetric assay. The results show that the 8-hydroxyquinoline reagent was more favorable in terms of the minimized affects of the potential interferences present in the digested solutions, i.e., Fe³⁺, Si⁴⁺ and F^-. The calibration curve was constructed from 0.10 mg/L to 3.00 mg/L with the goodness of linearity (R² = 0.9996). The limits of detection and quantification (LOD and LOQ) were estimated, i.e., 0.029 mg/L and 0.087 mg/L, respectively. The 8-hydroxyquinoline was applied to real rock samples, demonstrating favorable precision (RSD = 0.34 %-1.8 %) and no remarkable differences were found compared to the inductively coupled plasma-mass spectrometry (ICP-MS) as a reference measurement approach.

• Analytical Science and Technology
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• v.37 no.2
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• pp.87-97
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• 2024

The appropriate intake of fluoride (F^-) is beneficial to human health; however, the over-consumption can result in various potentially harmful effects. This study compared different colorimetric reagents, i.e., aluminium-xylenol orange (Al-XO), zirconium-xylenol orange (Zr-XO), and zirconium-alizarin red S (Zr-ARS), for fluoride measurements by the UV-Vis, in terms of reaction mechanisms, method sensitivity, and interferences from aluminium and ferric ions. The colorimetric procedures were optimized, and the analytical methods were evaluated. The goodness of linearity (R² > 0.998) was obtained for all three assays within the concentration range of 1.0-20.0 mg/L fluoride in deionized water, in which the method sensitivity followed the descending order of Zr-XO > Al-XO > Zr-ARS. The Zr-XO was applied for determining the fluoride in different tea extracts in water (90 ℃ and 60-minute-brewing) and black tea demonstrated the highest fluoride content (3.0-3.6 mg/L). The effects of brewing time and temperature on the release of fluoride in the tea extracts were also investigated, indicating these are critical factors for the fluoride extraction. This study highlighted the application potentials of the UV-Vis measurement as a simple, convenient, and cheap analytical approach and discussed different colorimetric reagents used for fluoride determination in tea extracts in the context that the UV-Vis spectrophotometers are commonly equipped in most laboratories.

• Journal of Hydrogen and New Energy
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• v.35 no.2
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• pp.152-161
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• 2024

In this study, a fuel cell system for future defense unmanned vehicles was designed and validated. A Co/Al₂O₃-Ni foam catalyst for NaBH₄ hydrolysis was characterized using several analytical methods. A NaBH₄ hydrogen generation system with the Co/Al₂O₃-Ni foam catalyst continuously generated hydrogen at elevated reaction temperatures. The fuel cell system with the NaBH₄ hydrogen generation system was designed and tested. The performance of the fuel cell system was comparable to that of the fuel cell system using pure hydrogen. Therefore, the fuel cell system with the NaBH₄ hydrogen generation system is a suitable power source for future defense unmanned vehicles owing to its easy refueling and simple system.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.1
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• pp.1-13
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• 2024

This study assessed the efficacy of improving the accuracy of reservoir water level prediction models by employing automated machine learning models and efficient cross-validation methods for time-series data. Considering the inherent complexity and non-linearity of time-series data related to reservoir water levels, we proposed an optimized approach for model selection and training. The performance of twelve models was evaluated for the Obong Reservoir in Gangneung, Gangwon Province, using the TPOT (Tree-based Pipeline Optimization Tool) and four cross-validation methods, which led to the determination of the optimal pipeline model. The pipeline model consisting of Extra Tree, Stacking Ridge Regression, and Simple Ridge Regression showed outstanding predictive performance for both training and test data, with an R² (Coefficient of determination) and NSE (Nash-Sutcliffe Efficiency) exceeding 0.93. On the other hand, for predictions of water levels 12 hours later, the pipeline model selected through time-series split cross-validation accurately captured the change pattern of time-series water level data during the test period, with an NSE exceeding 0.99. The methodology proposed in this study is expected to greatly contribute to the efficient generation of reservoir water level predictions in regions with high rainfall variability.

• Geomechanics and Engineering
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• v.37 no.3
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• pp.223-241
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• 2024

Evaluating the performance of Tunnel Boring Machines (TBMs) stands as a pivotal juncture in the domain of hard rock mechanized tunneling, essential for achieving both a dependable construction timeline and utilization rate. In this investigation, three advanced artificial neural networks namely, gated recurrent unit (GRU), back propagation neural network (BPNN), and simple recurrent neural network (SRNN) were crafted to prognosticate TBM-rate of penetration (ROP). Drawing from a dataset comprising 1125 data points amassed during the construction of the Alborze Service Tunnel, the study commenced. Initially, five geomechanical parameters were scrutinized for their impact on TBM-ROP efficiency. Subsequent statistical analyses narrowed down the effective parameters to three, including uniaxial compressive strength (UCS), peak slope index (PSI), and Brazilian tensile strength (BTS). Among the methodologies employed, GRU emerged as the most robust model, demonstrating exceptional predictive prowess for TBM-ROP with staggering accuracy metrics on the testing subset (R² = 0.87, NRMSE = 6.76E-04, MAD = 2.85E-05). The proposed models present viable solutions for analogous ground and TBM tunneling scenarios, particularly beneficial in routes predominantly composed of volcanic and sedimentary rock formations. Leveraging forecasted parameters holds the promise of enhancing both machine efficiency and construction safety within TBM tunneling endeavors.

• Anatomy and Cell Biology
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• v.56 no.2
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• pp.179-184
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• 2023

The Functional endoscopic sinus surgery through transnasal approach is a common modality of treatment for disorders of the nasal cavity, paranasal air sinuses as well as cranial cavity. The olfactory fossa (OF) is located along the superior aspect of cribriform plate which varies in shape and depth. This variable measurement of the depth of OF is mostly responsible for greater risk of intracranial infiltration during endoscopic procedures in and around the nasal cavity. The morphology of frontal and ethmoid sinus (ES) vary from simple to complex. This cadaveric study is planned to improve the ability of the otolaryngologist, radiologist to understand the possible morphological variations and plan steps of less invasive "precision surgery" to have a safe and complication free procedures. A total of 37 human head regions were included in the study. For classification of OF, Modified Kero's classification was used. The size, shape and cells of frontal and ES were noted. We found, type II (60.8%) OF was more common followed by type I (29.7%) than type III (9.5%). The shape of frontal sinus was comma shaped (55.4%) followed by oval (18.9%) than irregular (16.2%). Most common two cells type of ES was seen in 50.0% of both anterior and posterior ES. Out of 74 ES, 8.1% of Onodi cells and 14.9% of agger nasi cells were seen.

• Journal of Microbiology and Biotechnology
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• v.33 no.11
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• pp.1506-1512
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• 2023

Quantitative analysis of adenosine triphosphate (ATP) has been widely used as a diagnostic tool in the food and medical industries. Particularly, the pathogenesis of a few diseases including inflammatory bowel disease (IBD) is closely related to high ATP concentrations. A bioluminescent D-luciferin/luciferase system, which includes a luciferase (FLuc) from the firefly Photinus pyralis as a key component, is the most commonly used method for the detection and quantification of ATP. Here, instead of isolating FLuc produced in recombinant Escherichia coli, we aimed to develop a whole-cell biocatalyst system that does not require extraction and purification of FLuc. To this end, the gene coding for FLuc was introduced into the genome of probiotic Saccharomyces boulardii using the CRISPR/Cas9-based genome editing system. The linear relationship (r² = 0.9561) between ATP levels and bioluminescence generated from the engineered S. boulardii expressing FLuc was observed in vitro. To explore the feasibility of using the engineered S. boulardii expressing FLuc as a whole-cell biosensor to detect inflammation biomarker (i.e., ATP) in the gut, a colitis mouse model was established using dextran sodium sulfate as a colitogenic compound. Our findings demonstrated that the whole-cell biosensor can detect elevated ATP levels during gut inflammation in mice. Therefore, the simple and powerful method developed herein could be applied for non-invasive IBD diagnosis.

• Journal of The Korean Institute of Defense Technology
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• v.5 no.1
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• pp.7-18
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• 2023

The military leader makes an operation plan to accomplish combat missions. The current doctrine for an operation planning requires the use of simple and clear procedures and methods that can be carried out with human effort under adverse conditions in the field. The work in the process of an operation planning can be said to be a series of decision-making, and the criteria for decision-making generally apply mission variables. However, detailed standards are not fixed as doctrine, but are creatively established and applied. However, for AI-based decision-making, it is necessary to formalize the criteria and the format used. This paper first aims to standardize various criteria and forms to present a method that can be used in a semi-automated assist system, and to seek a plan to artificialize it. To this end, mathematical models and decision-making methods established in the field of operations research were applied to improve efficiency.