• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.198-205
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• 2021

For twenty years, in the EU there has been a trend of a lack of maritime infrastructure and a redundance of the road one, which has a negative impact on the economy. The intermodal transport market structure in the EU has not changed over the past ten years. The stability of transport systems due to the lack of changes in the transport market remains under threat, affecting supply chains and networks through the optimization of warehousing and transportation costs. The research methodology is based on a quantitative assessment of cause-and-effect relations between economic growth and transport and logistics in the EU. A statistical analysis of security indicators, intermodal and modal transport, international trade in goods within the EU and in the world trade in goods, the dynamics of GDP of the EU countries, the level of openness of the EU economy, investment and maintenance costs of different modes of transport and infrastructure has been carried out. The results show that in 2000- 2010 there were positive changes in the transport and logistics infrastructure of the EU, which had a positive effect on trade, openness of the economy of the EU, GDP growth. However, at that time, negative effects of environmental impact and the load on road and rail transport were accumulating. Investment in different modes of transport is limited, and technical maintenance and infrastructure maintenance costs form a significant part of GDP of the EU. A slowdown in economic growth leads to budget constraints and infrastructure financing gap. As a result, the freight and passenger intermodal and modal transport market structure remains virtually unchanged. The load on rail and road transport remains stable, despite the reduced level of transport hazards. Transport productivity has declined over the past ten years. Herewith, the intensification of trade and the openness of the EU economies require constant modernization and innovative renewal. The EU policy in this direction remains normative, uncontrolled, which is reflected in investment differences within the EU and maintenance costs.

• Korea Journal of Cosmetic Science
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• v.2 no.1
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• pp.1-10
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• 2020

Protection mechanisms for skin damage of ultraviolet (UV) absorbers in personal care products for protection against UV are well studied, but not for hair protection. The purpose of this study is to describe and compare the changes of physical property produced in human hair by doses of the UV-B exposure causing protein degradation. To observe the change of physical properties in hair, the experimental intensity of UV-B exposure has been established on the basis of statistical data from official meterological administration as daily one hour sunlight exposure for two weeks. Polysilicone-15, ethylhexyl methoxycinnamate (OMC), and octocrylene were employed for UV-B absorber, and those were treated to hair swatch by rubbing wash through shampoo and conditioner. Bending rigidity displayed kinetically successive reduction at high doses of UV exposure up to the 8,000 s, and exhibited different level at each sample of UV-B absorber. However, the values of Bossa Nova Technologies (BNT) for shinning factor were already saturable at the 2,000 s exposure except that treated with polysilicone-15. The differential scanning calorimetry (DSC) to measure a strength of inner protein produces a successive reduction of enthalpy as like a reduction of bending rigidity upon UV exposure. Surface roughness from lateral force microscope (LFM) acquired immediately after UV exposure show a saturable frictional voltage which has been also found in a saturable BNT data as the time of UV exposure increases. Through researching the DSC and the LFM, shinning of hair was much correlated to the protein damage at the surface, and bending rigidity could be regulated by the protein structural damage inside hair. Therefore, the optimization of efficient strategy for simultaneous prevention of hair protein on the surface and internal hair was required to maintain physical properties against UV.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.273-286
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• 2021

In recent years, as human casualties and property damage caused by hazardous waves have increased in the East Sea, precise wave prediction skills have become necessary. In this study, the Simulating WAves Nearshore (SWAN) third-generation numerical wave model was calibrated and optimized to enhance the accuracy of winter storm wave prediction in the East Sea. We used Source Term 6 (ST6) and physical observations from a large-scale experiment conducted in Australia and compared its results to Komen's formula, a default in SWAN. As input wind data, we used Korean Meteorological Agency's (KMA's) operational meteorological model called Regional Data Assimilation and Prediction System (RDAPS), the European Centre for Medium Range Weather Forecasts' newest 5th generation re-analysis data (ERA5), and Japanese Meteorological Agency's (JMA's) meso-scale forecasting data. We analyzed the accuracy of each model's results by comparing them to observation data. For quantitative analysis and assessment, the observed wave data for 6 locations from KMA and Korea Hydrographic and Oceanographic Agency (KHOA) were used, and statistical analysis was conducted to assess model accuracy. As a result, ST6 models had a smaller root mean square error and higher correlation coefficient than the default model in significant wave height prediction. However, for peak wave period simulation, the results were incoherent among each model and location. In simulations with different wind data, the simulation using ERA5 for input wind datashowed the most accurate results overall but underestimated the wave height in predicting high wave events compared to the simulation using RDAPS and JMA meso-scale model. In addition, it showed that the spatial resolution of wind plays a more significant role in predicting high wave events. Nevertheless, the numerical model optimized in this study highlighted some limitations in predicting high waves that rise rapidly in time caused by meteorological events. This suggests that further research is necessary to enhance the accuracy of wave prediction in various climate conditions, such as extreme weather.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.4
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• pp.113-127
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• 2021

Topic modeling has been receiving much attention in academic disciplines in recent years. Topic modeling is one of the applications in machine learning and natural language processing. It is a statistical modeling procedure to discover topics in the collection of documents. Recently, there have been many attempts to find out topics in diverse fields of academic research. Although the first Department of Industrial Engineering (I.E.) was established in Hanyang university in 1958, Korean Institute of Industrial Engineers (KIIE) which is truly the most academic society was first founded to contribute to research for I.E. and promote industrial techniques in 1974. Korean Society of Industrial and Systems Engineering (KSIE) was established four years later. However, the research topics for KSIE journal have not been deeply examined up until now. Using topic modeling algorithms, we cautiously aim to detect the research topics of KSIE journal for the first half of the society history, from 1978 to 1999. We made use of titles and abstracts in research papers to find out topics in KSIE journal by conducting four algorithms, LSA, HDP, LDA, and LDA Mallet. Topic analysis results obtained by the algorithms were compared. We tried to show the whole procedure of topic analysis in detail for further practical use in future. We employed visualization techniques by using analysis result obtained from LDA. As a result of thorough analysis of topic modeling, eight major research topics were discovered including Production/Logistics/Inventory, Reliability, Quality, Probability/Statistics, Management Engineering/Industry, Engineering Economy, Human Factor/Safety/Computer/Information Technology, and Heuristics/Optimization.

• New & Renewable Energy
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• v.17 no.2
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• pp.50-58
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• 2021

We proposed an MEA development methodology that accurately measures intrinsic MEA performance while considering the uneven reaction environments formed inside a large-area BP. To facilitate measurement of the inherent MEA performance, we miniaturized the active area of the MEA to 3 cm², and prepared two MEAs with different ionomer contents of 0.65 and 0.80 (I/C). By simulating the operating conditions of a 100 cm² BP at the inlet (I), center (C), and outlet (O), the oxygen concentration and relative humidity were determined to be 20.7, 13.8, 11.7%, and 50, 66.1, and 70.1% respectively. We measured the performance and electrochemical analysis of the prepared MEAs under the three simulated conditions. Based on the results of statistical analysis of the evaluated MEA performance data, I/C 0.65 MEA had a higher average performance and lower performance deviation than I/C 0.80 MEA. Hence, it can be concluded that an I/C 0.65 MEA is a more effective MEA for large-area BP. Based on the above research process, we confirmed the effectiveness of the proposed MEA development methodology.

• Advances in nano research
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• v.12 no.5
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• pp.515-527
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• 2022

The application of fibers in concrete obviously enhances the properties of concrete, also the application of natural fibers in concrete is raising due to the availability, low cost and environmentally friendly. Besides, predicting the mechanical properties of concrete in general and shear strength in particular is highly significant in concrete mixture with fiber nanocomposite reinforced concrete (FRC) in construction projects. Despite numerous studies in shear strength, determining this strength still needs more investigations. In this research, Adaptive Neuro-Fuzzy Inference System (ANFIS) have been employed to determine the strength of reinforced concrete with fiber. 180 empirical data were gathered from reliable literature to develop the methods. Models were developed, validated and their statistical results were compared through the root mean squared error (RMSE), determination coefficient (R²), mean absolute error (MAE) and Pearson correlation coefficient (r). Comparing the RMSE of PSO (0.8859) and ANFIS (0.6047) have emphasized the significant role of structural parameters on the shear strength of concrete, also effective depth, web width, and a clear depth rate are essential parameters in modeling the shear capacity of FRC. Considering the accuracy of our models in determining the shear strength of FRC, the outcomes have shown that the R² values of PSO (0.7487) was better than ANFIS (2.4048). Thus, in this research, PSO has demonstrated better performance than ANFIS in predicting the shear strength of FRC in case of accuracy and the least error ratio. Thus, PSO could be applied as a proper tool to maximum accuracy predict the shear strength of FRC.

• Smart Structures and Systems
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• v.29 no.1
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• pp.117-127
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• 2022

Data anomalies seriously threaten the reliability of the bridge structural health monitoring system and may trigger system misjudgment. To overcome the above problem, an efficient and accurate data anomaly detection method is desiderated. Traditional anomaly detection methods extract various abnormal features as the key indicators to identify data anomalies. Then set thresholds artificially for various features to identify specific anomalies, which is the artificial experience method. However, limited by the poor generalization ability among sensors, this method often leads to high labor costs. Another approach to anomaly detection is a data-driven approach based on machine learning methods. Among these, the bidirectional long-short memory neural network (BiLSTM), as an effective classification method, excels at finding complex relationships in multivariate time series data. However, training unprocessed original signals often leads to low computation efficiency and poor convergence, for lacking appropriate feature selection. Therefore, this article combines the advantages of the two methods by proposing a deep learning method with manual experience statistical features fed into it. Experimental comparative studies illustrate that the BiLSTM model with appropriate feature input has an accuracy rate of over 87-94%. Meanwhile, this paper provides basic principles of data cleaning and discusses the typical features of various anomalies. Furthermore, the optimization strategies of the feature space selection based on artificial experience are also highlighted.

• Atmosphere
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• v.33 no.5
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• pp.519-530
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• 2023

Deep convection can make adverse effects on safe and efficient aviation operations by causing various weather hazards such as convectively-induced turbulence, icing, lightning, and downburst. To prevent such damage, it is necessary to accurately predict spatiotemporal distribution of deep convective area near the airport and airspace. This study developed a new index, the Aviation Convective Index (ACI), for deep convection, using the operational global Unified Model of the Korea Meteorological Administration. The ACI was computed from combination of three different variables: 3-hour maximum of Convective Available Potential Energy, averaged Outgoing Longwave Radiation, and accumulative precipitation using the fuzzy logic algorithm. In this algorithm, the individual membership function was newly developed following the cumulative distribution function for each variable in Korean Peninsula. This index was validated and optimized by using the 1-yr period of radar mosaic data. According to the Receiver Operating Characteristics curve (AUC) and True Skill Score (TSS), the yearly optimized ACI (ACI_YrOpt) based on the optimal weighting coefficients for 1-yr period shows a better skill than the no optimized one (ACI_NoOpt) with the uniform weights. In all forecast time from 6-hour to 48-hour, the AUC and TSS value of ACI_YrOpt were higher than those of ACI_NoOpt, showing the improvement of averaged value of AUC and TSS by 1.67% and 4.20%, respectively.

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Journal of Technologic Dentistry
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• v.46 no.3
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• pp.84-92
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• 2024

Purpose: To evaluate the effect of post-curing on the three-dimensional (3D) accuracy of artificial teeth, denture bases, and denture base monoblock manufactured using digital light processing (DLP) technology. Methods: Using an edentulous model, a 3D design was made for complete dentures. Three groups were printed by DLP: artificial teeth, denture bases, and denture base monoblock. The models were scanned, subjected to post-curing, and scanned again. Three-dimensional analysis was performed based on the post-treatment differences among the three groups. Statistical analysis was performed using SPSS Statistics ver. 22.0 (IBM), and the Mann-Whitney U-test and Kruskal-Wallis test were employed as nonparametric tests. Results: The complete denture monoblock (CM) and complete denture artificial teeth (CA) groups showed the lowest and highest errors at 15.13 and 23.37 ㎛, respectively. The groups did not show significant differences (p>0.05). In the significance test among the three groups, no significance was found in the CA group; however, significant differences were found between the complete denture base (CB) and CM groups. In addition, the three groups showed significant differences (p<0.05). Conclusion: Although deformation may occur during the post-curing process, it is within the clinically acceptable range. Future comparative studies using different 3D printers and searching for ways to minimize errors through optimization of the post-curing process are warranted.