• Advances in aircraft and spacecraft science
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• 제9권5호
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• pp.367-375
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• 2022

Modeling the properties of complex alloys such as nickel superalloys is an extremely challenging scientific and engineering task. The model should take into account a large number of uncorrelated factors, for many of which information may be missing or vague. The individual contribution of one or another chemical element out of a dozen possible ligants cannot be determined by traditional methods. Moreover, there are no general analytical models describing the influence of elements on the characteristics of alloys. Artificial neural networks are one of the few statistical modeling tools that can account for many implicit correlations and establish correspondences that cannot be identified by other more familiar mathematical methods. However, such networks require careful tuning to achieve high performance, which is time-consuming. Data preprocessing can make model training much easier and faster. This article focuses on combining physics-based deep network configuration and input data engineering to simulate the solvus temperature of nickel superalloys. The used deep artificial neural network shows good simulation results. Thus, this method of numerical simulation can be easily applied to such problems.

• Membrane and Water Treatment
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• 제14권3호
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• pp.141-146
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• 2023

Algal organic matters (AOMs) are challenging to remove using traditional water treatment methods. Additionally, they are recognized as disinfection by product (DBP) precursors during the chlorination process. These compounds have the potential to seriously harm aquatic creatures. Despite the fact that AOMs and DBPs formed from algae can harm aquatic species by impairing their cognitive function and causing behavioral problems, only a few studies on the effects of AOMs and associated DBPs have been conducted. To assess the impact of extracellular organic materials (EOMs) produced by three different hazardous algal species and the chlorinated EOMs on zebrafish, this study used fish acute embryo toxicity (FET) and cognitive function tests. With rising EOM concentrations, the embryo's survival rate and mental capacity both declined. Of the three algal species, the embryo exposed to Microcystis aeruginosa EOM exhibited the lowest survival rate. On the other hand, the embryo exposed to EOMs following chlorination demonstrated a drop in CT values in both the survival rate and cognitive ability. These findings imply that EOMs and EOMs treated with chlorine may have detrimental effects on aquatic life. Therefore, an effective EOM management is needed in aquatic environment.

• International conference on construction engineering and project management
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.904-911
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• 2022

Occlusion is one of the most challenging problems for computer vision-based construction monitoring. Due to the intrinsic dynamics of construction scenes, vision-based technologies inevitably suffer from occlusions. Previous researchers have proposed the occlusion handling methods by leveraging the prior information from the sequential images. However, these methods cannot be employed for construction object detection in non-sequential images. As an alternative occlusion handling method, this study proposes a data augmentation-based framework that can enhance the detection performance under occlusions. The proposed approach is specially designed for rebar occlusions, the distinctive type of occlusions frequently happen during construction worker detection. In the proposed method, the artificial rebars are synthetically generated to emulate possible rebar occlusions in construction sites. In this regard, the proposed method enables the model to train a variety of occluded images, thereby improving the detection performance without requiring sequential information. The effectiveness of the proposed method is validated by showing that the proposed method outperforms the baseline model without augmentation. The outcomes demonstrate the great potential of the data augmentation techniques for occlusion handling that can be readily applied to typical object detectors without changing their model architecture.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권3호
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• pp.740-753
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• 2023

Text recognition in natural scene images is a challenging problem in computer vision. The accurate identification of ship number characters can effectively improve the level of ship traffic management. However, due to the blurring caused by motion and text occlusion, the accuracy of ship number recognition is difficult to meet the actual requirements. To solve these problems, this paper proposes a dual-branch network based on the CRNN identification network. The network couples image restoration and character recognition. The CycleGAN module is used for blur restoration branch, and the Pix2pix module is used for character occlusion branch. The two are coupled to reduce the impact of image blur and occlusion. Input the recovered image into the text recognition branch to improve the recognition accuracy. After a lot of experiments, the model is robust and easy to train. Experiments on CTW datasets and real ship maps illustrate that our method can get more accurate results.

• Smart Structures and Systems
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• 제31권2호
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• pp.199-212
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• 2023

Nowadays fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages. Generalized type-2 fuzzy sets allow for a third dimension to capture higher order uncertainty and therefore offer a very powerful model for uncertainty handling in real world applications. With the recent advances that allowed the performance of general type-2 fuzzy logic controllers to increase, it is now expected to see the widespread of type-2 fuzzy logic controllers to many challenging applications in particular in problems of structural control, that is the case study in this paper. It should be highlighted that this is the first application of general type-2 fuzzy approach in civil structures. In the following, general type-2 fuzzy logic controller (GT2FLC) will be used for active control of a 9-story nonlinear benchmark building. The design of type-1 and interval type-2 fuzzy logic controllers is also considered for the purpose of comparison with the GT2FLC. The performance of the controller is validated through the computer simulation on MATLAB. It is demonstrated that extra design degrees of freedom achieved by GT2FLC, allow a greater potential to better model and handle the uncertainties involved in the nature of earthquakes and control systems. GT2FLC outperforms successfully a control system that uses T1 and IT2 FLCs.

• Nuclear Engineering and Technology
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• 제54권12호
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• pp.4412-4421
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• 2022

The pressure measurement in the high-temperature liquid metal system, such as Sodium-cooled Fast Reactor(SFR), is important and yet it is very challenging due to its nature. The measuring pressure is relatively at low range and the applied temperature varies in wide range. Moreover, the pressure transfer material in impulse line needs to considered the high temperature condition. The conventional diaphragm-based approach cannot be used for it is impossible to remove the effect of thermal expansion. In this paper, the Fiber Bragg Grating(FBG) sensor-based pressure measuring concept is suggested that it is free of problems induced by the thermal expansion. To verify this concept, a prototype was fabricated and tested in an appropriate conditions. The uncertainty analysis result of the experiment is also included. The final result of this study clearly showed that the FBG-based pressure transmitter system is applicable to the extreme environment, such as SFR and any other high-temperature liquid metal system and the measurement uncertainty is within reasonable range.

• Nuclear Engineering and Technology
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• 제55권2호
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• pp.756-768
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• 2023

Neutron transport calculations are extremely challenging due to the high computational cost of large and complex problems. A multilevel octree grid algorithm (MLTG) of discrete ordinates method was developed to improve the modeling accuracy and simulation efficiency on 3-D Cartesian grids. The Balakovo-3 VVER-1000 neutron dosimetry benchmark is calculated to verify and validate this numerical technique. A simplified S₂ synthetic acceleration is used in the MLTG calculation method to improve the convergence of the source iterations. For the triangularly arranged fuel pins, we adopt a source projection algorithm to generate pin-by-pin source distributions of hexagonal assemblies. MLTG provides accurate geometric modeling and flexible fixed source description at a lower cost than traditional Cartesian grids. The total number of meshes is reduced to 1.9 million from the initial 9.5 million for the Balakovo-3 model. The numerical comparisons show that the MLTG results are in satisfactory agreement with the conventional S_N method and experimental data, within the root-mean-square errors of about 4% and 10%, respectively. Compared to uniform fine meshing, approximately 70% of the computational cost can be saved using the MLTG algorithm for the Balakovo-3 computational model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1466-1488
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• 2022

Recently, the importance and necessity of artificial intelligence (AI), especially machine learning, has been emphasized. In fact, studies are actively underway to solve complex and challenging problems through the use of AI systems, such as intelligent CCTVs, intelligent AI security systems, and AI surgical robots. Information security that involves analysis and response to security vulnerabilities of software is no exception to this and is recognized as one of the fields wherein significant results are expected when AI is applied. This is because the frequency of malware incidents is gradually increasing, and the available security technologies are limited with regard to the use of software security experts or source code analysis tools. We conducted a study on MalDC, a technique that converts malware into images using machine learning, MalDC showed good performance and was able to analyze and classify different types of malware. MalDC applies a preprocessing step to minimize the noise generated in the image conversion process and employs an image augmentation technique to reinforce the insufficient dataset, thus improving the accuracy of the malware classification. To verify the feasibility of our method, we tested the malware classification technique used by MalDC on a dataset provided by Microsoft and malware data collected by the Korea Internet & Security Agency (KISA). Consequently, an accuracy of 97% was achieved.

• International conference on construction engineering and project management
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• The 9th International Conference on Construction Engineering and Project Management
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• pp.196-203
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• 2022

The resource constrained scheduling problem (RCSP) constitutes one of the most challenging problems in Project Management, as it combines multiple parameters, contradicting objectives (project completion within certain deadlines, resource allocation within resource availability margins and with reduced fluctuations), strict constraints (precedence constraints between activities), while its complexity grows with the increase in the number of activities being executed. Due to the large solution space size, this work investigates the application of Genetic Algorithms to approximate the optimal resource alolocation and obtain optimal trade-offs between different project goals. This analysis uses the cost of exceeding the daily resource availability, the cost from the day-by-day resource movement in and out of the site and the cost for using resources day-by-day, to form the objective cost function. The model is applied in different case studies: 1 project consisting of 10 activities, 4 repetitive projects consisting of 40 activities in total and 16 repetitive projects consisting of 160 activities in total, in order to evaluate the effectiveness of the algorithm in different-size solution spaces and under alternative optimization criteria by examining the quality of the solution and the required computational time. The case studies 2 & 3 have been developed by building upon the recurrence of the unit/sub-project (10 activities), meaning that the initial problem is multiplied four and sixteen times respectively. The evaluation results indicate that the proposed model can efficiently provide reliable solutions with respect to the individual goals assigned in every case study regardless of the project scale.

• International conference on construction engineering and project management
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• The 5th International Conference on Construction Engineering and Project Management
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• pp.616-623
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• 2013

One of the oldest, most common engineering problems is measuring the dimensions of different objects and the distances between locations. In AEC/FM, related uses vary from large-scale applications such as measuring distances between cities to small-scale applications such as measuring the depth of a crack or the width of a welded joint. Within the last few years, advances in applying new technologies have prompted the development of new measuring devices such as ultrasound and laser-based measurers. Because of wide varieties in type, associated costs, and levels of accuracy, the selection of an optimal measuring technology is challenging for construction engineers and facility managers. To tackle this issue, we present an overview of various measuring technologies adopted by experts in the area of AEC/FM. As the next step, to evaluate the performance of these technologies, we select one indoor and one outdoor case and measure several dimensions using six categories of technologies: tapes, total stations, laser measurers, ultrasound devices, laser scanners, and image-based technologies. Then we evaluate the results according to various metrics such as accuracy, ease of use, operation time, associated costs, compare these results, and recommend optimal technologies for specific applications. The results also revealed that in most applications, computer vision-based technologies outperform traditional devices in terms of ease of use, associated costs, and accuracy.