• Computers and Concrete
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• v.33 no.4
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• pp.425-443
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• 2024

Floods are among the most common natural hazards in urban areas. To mitigate the problems caused by flooding, unstructured data such as images and videos collected from closed circuit televisions (CCTVs) or unmanned aerial vehicles (UAVs) have been examined for flood management (FM). Many computer vision (CV) techniques have been widely adopted to analyze imagery data. Although some papers have reviewed recent CV approaches that utilize UAV images or remote sensing data, less effort has been devoted to studies that have focused on CCTV data. In addition, few studies have distinguished between the main research objectives of CV techniques (e.g., flood depth and flooded area) for a comprehensive understanding of the current status and trends of CV applications for each FM research topic. Thus, this paper provides a comprehensive review of the literature that proposes CV techniques for aspects of FM using ground camera (e.g., CCTV) data. Research topics are classified into four categories: flood depth, flood detection, flooded area, and surface water velocity. These application areas are subdivided into three types: urban, river and stream, and experimental. The adopted CV techniques are summarized for each research topic and application area. The primary goal of this review is to provide guidance for researchers who plan to design a CV model for specific purposes such as flood-depth estimation. Researchers should be able to draw on this review to construct an appropriate CV model for any FM purpose.

• Geomechanics and Engineering
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• v.39 no.1
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• pp.1-11
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• 2024

Geotechnical parameter estimation is critical to the design, performance, safety, and cost and schedule management in Tunnel Boring Machine projects. Since these parameters vary within a certain range, relying on mean values for evaluation introduces significant risks to the project. Due to the non-homogeneous characteristics of geological formation, data may not exhibit a normal distribution and the presence of outliers might be deceptive. Therefore, the use of reliable analyses and simulation models is inevitable in the course of the data evaluation process. Advanced modeling techniques enable comprehensive analysis of the project data and allowing to model the uncertainty in geotechnical parameters. This study involves using Monte Carlo Simulation method to predict probabilistic distributions of field data, and therefore, establish a basis for designs and in turn to minimize project risks. In the study, 166 sets of geotechnical data Obtained from 35 boreholes including Standard Penetration Test, Limit Pressure, Liquid Limit, and Plastic Limit values, which are mostly utilized parameters in estimating project requirements, were used to estimate the geotechnical data distribution of the study field. In this context, firstly, the data was subjected to multi-parameter linear regression and variance analysis. Then, the obtained equations were implemented into a Monte Carlo Simulation, and probabilistic distributions of the geotechnical data of the field were simulated and corresponding to the 90% probability range, along with the minimum and maximum values at the 5% probability levels presented. Accordingly, while the average SPT N30 value is 42.86, but the highest occurrence rate is 50.81. For Net Limit Pressure, the average field data is 17.07 kg/cm², with the maximum occurrence between 9.6 kg/cm² and 13.7 kg/cm². Similarly, the average Plastic Limit value is 22.32, while the most probable value is 20.6. The average Liquid Limit value is 56.73, with the highest probability at 54.48, as indicated in the statistical data distribution. Understanding the percentage distribution of data likely to be encountered in the project allows for accurate forecasting of both high and low probability scenarios, offering a significant advantage, particularly in ordering TBM requirements.

• Korean Journal of Agricultural Science
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• v.51 no.2
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• pp.133-146
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• 2024

Selection of gear reduction ratio is essential for machine design to ensure suitable power and speed during agricultural operations. The goal of the study was to evaluate the gear reduction ratio for a 1.6 kW four-wheel-drive (4WD) multi-purpose agricultural electric vehicle platform using workload data under different off-road conditions. A data acquisition system was fabricated to collect workload (torque) of the vehicle acting on the gear shaft. Field tests were performed under three driving surfaces (asphalt, concrete, and grassland), payload operations (981, 2,942, and 4,903 N), and slope conditions (0 - 4°, 4 - 8°, and 8 - 12°), respectively. Commercial speed reduction gear phases were attached to the input shaft of the vehicle powertrain. The maximum required torque was recorded as 37.5 Nm at a 4,903 N load with 8 - 12° slope levels, and the minimum torque was 12.32 Nm at 0 - 4° slope levels with a 981 Nm load for a 4 km/h speed on asphalt, concrete, and grassland roads. Based on the operating load condition and motor torque and rotational speed (TN) curve, the minimum and maximum gear reduction ratios were chosen as 1 : 50 and 1 : 64, respectively. The selected motor satisfied power requirements by meeting all working torque criteria with the gear reduction ratios. The chosen motor with a gear reduction ratio of 1 : 50 was suitable to fit with the motor T-N curve, and produced the maximum speeds and loads needed for driving and off-road activities. The findings of the study would assist in choosing a suitable gear reduction ratio for electric vehicle multi-purpose field operations.

• The Journal of Bigdata
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• v.8 no.2
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• pp.201-222
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• 2023

As the e-Commerce gets increased based on the platform, a lot of small and medium sized sellers have tried to develop the more effective strategies to maximize the profit. In order to increase the profitability, it is quite important to make the strategic decisions based on the range of promotion, discount rate and categories of products. This research aims to develop the business intelligence application which can help sellers of e-Commerce platform make better decisions. To decide whether or not to promote, it is needed to predict the level of increase in sales after promotion. I n this research, we have applied the various machine learning algorithm such as MLP(Multi Layer Perceptron), Gradient Boosting Regression, Random Forest, and Linear Regression. Because of the complexity of data structure and distinctive characteristics of product categories, Random Forest and MLP showed the best performance. It seems possible to apply the proposed approach in this research in support the small and medium sized sellers to react on the market changes and to make the reasonable decisions based on the data, not their own experience.

• Structural Engineering and Mechanics
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• v.91 no.6
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• pp.643-656
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• 2024

The prediction of the moment rotation behavior of semi-rigid connections has been the subject of extensive research. However, to improve the accuracy of these predictions, there is a growing interest in employing machine learning algorithms. This paper investigates the effectiveness of using Multi-gene genetic programming (MGGP) to predict the moment-rotation behavior of flush-end plate connections compared to that of artificial neural networks (ANN) and previous studies. It aims to automate the process of determining the most suitable equations to accurately describe the behavior of these types of connections. Experimental data was used to train ANN and MGGP. The performance of the models was assessed by comparing the values of coefficient of determination (R²), maximum absolute error (MAE), and root-mean-square error (RMSE). The results showed that MGGP produced more accurate, reliable, and general predictions compared to ANN and previous studies with an R² exceeding 0.99, an RMSE of 6.97, and an MAE of 38.68, highlighting its advantages over other models. The use of MGGP can lead to better modeling and more precise predictions in structural design. Additionally, an experimentally-based regression analysis was conducted to obtain the rotational capacity of FECs. A new equation was proposed and compared to previous ones, showing significant improvement in accuracy with an R² score of 0.738, an RMSE of 0.014, and an MAE of 0.024.

• Journal of the Society of Disaster Information
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• v.20 no.3
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• pp.593-600
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• 2024

Purpose: In this study, fundamental research was conducted to capture sparks generated during the arc welding process. Method: To this end, a mock-up collection device was developed, consisting of a blower for suction, a nozzle, and a filter and magnet at the suction point. To analyze the correlation between the shape of the filter and its magnetic properties, the porosity and pore size of both carbon filters and Colgate filters were quantified under conditions of a welding machine capacity of 1,800W and a welding time of 70s. The collection efficiency of sparks was calculated for magnetic strengths of 2.6, 3.4, and 4.05kgf. Result and Conclusion: As a result, empirical formulas were derived for the blower's suction capacity, magnetic strength, porosity, and pore diameter for capturing sparks, with experimental results confirming consistency within ± 10%. The findings of this study are expected to provide a quantitative design approach for collection devices that can minimize the risk of fire spread associated with welding operations at construction sites.

• The Journal of Korean Academy of Prosthodontics
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• v.47 no.1
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• pp.46-52
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• 2009

Statement of problem: Recently, titanium has become popular as superstructure material in implant dentistry because titanium superstructure can be easily milled by means of computer-aided design and manufacture (CAD/CAM) technique. But retention form such as nail head or bead cannot be cut as a result of technical limitation of CAD/CAM milling and bond strength between titanium and porcelain is not as strong as that of conventional gold or metal alloy. Purpose: The objective of this study was to evaluate the shear bond strength of three different materials: heat curing resin, composite resin, porcelain which were bonded to grade II commercially pure Titanium (CP-Ti). Material and methods: Thirty seven CP-Ti discs with 9 mm diameter, 10 mm height were divided into three groups and were bonded with heat curing resin (Lucitone 199), indirect composite resin (Sinfony), and porcelain (Triceram) which were mounted in a former with 7 mm diameter and 1 mm height. Samples were thermocycled for 1000 cycles at between $5-55^{\circ}C$. Shear bond strength (MPa) was measured with Instron Universal Testing Machine with cross head speed of 1 mm/min. The failure pattern was observed at the fractured surface and divided into adhesive, cohesive, and combination failure. The data were analyzed by one-way ANOVA and Scheffe's multiple range test (${\alpha}=0.05$). Results: Lucitone 199 ($17.82{\pm}5.13\;MPa$) showed the highest shear bond strength, followed by Triceram ($12.97{\pm}2.11\;MPa$), and Sinfony ($6.00{\pm}1.31\;MPa$). Most of the failure patterns in Lucitone 199 and Sinfony group were adhesive failure, whereas those in Triceram group were combination failure. Conclusion: Heat curing resin formed the strongest bond to titanium which is used as a CAD/CAM milling block. But the bond strength is still low compared with the bond utilizing mechanical interlocking and there are many adhesive failures which suggest that more studies to enhance bond strength are needed.

• Journal of Distribution Science
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• v.7 no.4
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• pp.47-56
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• 2009

The purpose of this study is to present an alternative improving the efficient and reasonable of the physical distribution system management is influenced by many factors. Therefore, the study depends on the documentary method and survey method to achieve the purpose of this study. The major components of a physical distribution system are refers to as elements, include warehouse·storage system, transportation system, inventory system, physical distribution information system. The factors used in this study are ① factor of product(quality·A/S·added value of product·adaption of product·technical competitive power to other enterprises), ② factor of market(market channel·kinds of customer·physical distribution share), ③ factor of warehouse·storage(warehouse design·size·direction·storage ability·warehouse quality), ④ factor of transportation(promptness·reliability·responsibility·kinds of transportation·cooperation united transportation system·national transportation network), ⑤ factor of packaging (packaging design·material·educating program·pollution degree measure program), ⑥ factor of inventory(ordinary inventory criterion·consistence for inventories record), ⑦ factor of unloaded(unloaded machine·having machine ratio), ⑧ factor of information system (physical distribution quantity analysis·usable computer part), ⑨ factor of physical distribution cost(sales ratio to product) ⑩ factor of physical distribution system(physical distribution center etc). The implication of this study can be summarized as follows: ① In firms that have not adopted a systems integrative approach, physical distribution is a fragmented and often uncoordinated set of activities spread throughout various functions with function having its own set of priorities and measurements. ② The physical distribution is recognized as more an important strategic factor than a simple cost reduction factor, ③ It can be used a strategic competition tool to enterprise.

• Korean Journal of Construction Engineering and Management
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• v.13 no.6
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• pp.143-152
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• 2012

As the Public Procurement Service announced the compulsory of BIM adaption in every public construction from 2016, the importance of BIM is increasing. Besides, automatic code checking takes significance in terms of the quality control for BIM based design. In this study, rule-sets were defined for Act on the Promotion and Guarantee of Access for the Disabled, the Aged, and Pregnant Women to Facilities and Information. Three analytic steps were suggested to shortlist the objective clauses from the entire code; the frequency analysis using project reviews for architectural code compliance, the clause analysis on quantifiability, and the analysis for model checking possibilities. The shortlisted clauses were transformed into the machine readable rule-set definition. A case study was conducted to verify the adaptiveness and consistency of rule-set definitions. In future study, it is required the methodologies of selecting objective clauses to be specified and its indicators to be quantified. Also case studies should be performed to determine the pre-conditions in modeling and to check interoperability issues and other possible errors in models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.482-489
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• 2020

This study conducted a prototype development and evaluation by performing die-casting mold design, mold manufacturing, and injection condition optimization based on flow and solidification analysis to meet the needs of the coupling parts produced by die casting. Through flow analysis, the injection conditions suitable for 100% filling in the cavity were found to be a molten metal temperature of 670 ℃, injection speed of 1.164 m/s, and filling pressure of 6.324~18.77 MPa. In addition, solidification close to 100 % occurred in all four cavities when the solidification rate was 69.47 %. A defect inspection on the surface and inside the product revealed defects, such as poor molding and pores. In addition, the dimensions of the injected product were within the target tolerance and showed good results. Through the feedback of the results of flow and solidification analysis, it was possible to optimize the mold design, and the injection optimization conditions were confirmed to be a total cycle time of approximately 6.5 seconds. Good quality carrier parts with an average surface hardness of approximately 45 mm from the gate measured at 97.48(Hv) could be produced.