• Computers and Concrete
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• v.28 no.5
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• pp.465-478
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• 2021

On the premise of ensuring that the static performance of the concrete spreader is met, the first-order natural frequency of the concrete spreader is increased, and the weight of the main beam is reduced. ANSYS is used as an analysis tool to perform modal analysis on the concrete spreader. The natural frequency, mode shape and modal test verification will be obtained to ensure the accuracy of finite element model analysis. Using the ANSYS designxplorer module, the size of the main beam is set, and the response surface model between the parameter variables and the optimization objective is established according to the experimental design points. Screening algorithm and MOGA algorithm are used to multi-optimize the stress, first-order natural frequency and girder weight, and the optimal solution is obtained by comparison. The results of modal analysis are consistent with those of the experiment, and a set of optimal solutions is obtained through the optimization algorithm. The optimal solution obtained can meet the purpose of increasing the first-order natural frequency of the concrete spreader and reducing the weight of the main beam under the premise of ensuring the overall dynamic and static performance of the concrete spreader.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3638-3657
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• 2022

In urban vehicular edge computing (VEC) environments, one edge server always serves many task requests in its coverage which results in the resource-constrained problem. To resolve the problem and improve system utilization, we first design a general hierarchical resource management framework based on typical VEC network structures. Following the framework, a specific interacting protocol is also designed for our decision algorithm. Secondly, a greedy bidding-based multi-hop task scheduling decision algorithm is proposed to realize effective task scheduling in resource-constrained VEC environments. In this algorithm, the goal of maximizing system utility is modeled as an optimization problem with the constraints of task deadlines and available computing resources. Then, an auction mechanism named greedy bidding is used to match task requests to edge servers in the case of multiple hops to maximize the system utility. Simulation results show that our proposal can maximize the number of tasks served in resource constrained VEC networks and improve the system utility.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.769-779
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• 2023

This paper describes an hp-angular adaptivity algorithm in the discrete ordinates method for Boltzmann transport applications with strong angular effects. This adaptivity uses discontinuous finite element quadrature sets with different degrees, which updates both angular mesh and the degree of the underlying discontinuous finite element basis functions, allowing different angular local refinement to be applied in space. The regular and goal-based error metrics are considered in this algorithm to locate some regions to be refined. A mapping algorithm derived by moment conservation is developed to pass the angular solution between spatial regions with different quadrature sets. The proposed method is applied to some test problems that demonstrate the ability of this hp-angular adaptivity to resolve complex fluxes with relatively few angular unknowns. Results illustrate that a reduction to approximately 1/50 in quadrature ordinates for a given accuracy compared with uniform angular discretization. This method therefore offers a highly efficient angular adaptivity for investigating difficult particle transport problems.

• Nuclear Engineering and Technology
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• v.55 no.3
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• Advances in Computational Design
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• v.7 no.4
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• pp.345-369
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• 2022

Infectious disease emergency hospitals are usually temporarily built during the pneumonia epidemic with higher requirements regarding diagnosis and treatment efficiency, hygiene and safety, and infection control.This study aims to identify how the Building Information Modeling (BIM) + Industrialized Building System (IBS) approach could rapidly deliver an infectious disease hospital and develop site epidemic spreading algorithms. Coronavirus-19 pneumonia construction site spreading algorithm model mind map and block diagram of the construction site epidemic spreading algorithm model were developed. BIM+IBS approach could maximize the repetition of reinforced components and reduce the number of particular components. Huoshenshan Hospital adopted IBS and BIM in the construction, which reduced the workload of on-site operations and avoided later rectification. BIM+IBS integrated information on building materials, building planning, building participants, and construction machinery, and realized construction visualization control and parametric design. The delivery of Huoshenshan Hospital was during the most critical period of the Coronavirus-19 pneumonia epidemic. The development of a construction site epidemic spreading algorithm provided theoretical and numerical support for prevention. The agent-based analysis on hospital evacuation observed "arched" congestion formed at the evacuation exit, indicating behavioral blindness caused by fear in emergencies.

• Steel and Composite Structures
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• v.49 no.1
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• pp.1-17
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• 2023

Based on Ahmadzadeh-Varvani hardening rule (A-V model), multiaxial ratcheting effect of Z2CND18.12N austenitic stainless steel is simulated by ABAQUS with user subroutine UMAT. The results show that the predicted results of the origin multiaxial A-V model are lower than the experimental data, and it is difficult to control ratcheting strain rate. In order to improve the predicted capability of A-V model, the A-V model is modified. In this study. Moreover, under the assumption of the von Mises yield criterion and normal plasticity flow rule, we develop a numerical algorithm of plastic strain with the improved model to implement the finite element calculation of the model. Internal iteration in the numerical algorithm was implemented with the Euler backward method, which calculated the trial strain for each equilibrium iteration using the consistent tangent matrix. With a user subroutine, the proposed model is programmed into ABAQUS for a user - executable version. By simulating the uniaxial ratcheting of a round bar made of Z2CND18.12N austenitic stainless steel, we observe that the predicted results simulated by ABAQUS with UMAT are compared with the experimental data. The predicted results of the improved multiaxial A-V model are consistent well with the experimental data.

• Earthquakes and Structures
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• v.27 no.3
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• pp.177-189
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• 2024

Accurately predicting the failure modes of reinforced concrete (RC) columns is essential for structural design and assessment. In this study, the challenges of imbalanced datasets and complex feature selection in machine learning (ML) methods were addressed through an optimized ML approach. By combining feature selection and oversampling techniques, the prediction of seismic failure modes in rectangular RC columns was improved. Two feature selection methods were used to identify six input parameters. To tackle class imbalance, the Borderline-SMOTE1 algorithm was employed, enhancing the learning capabilities of the models for minority classes. Eight ML algorithms were trained and fine-tuned using k-fold shuffle split cross-validation and grid search. The results showed that the artificial neural network model achieved 96.77% accuracy, while k-nearest neighbor, support vector machine, and random forest models each achieved 95.16% accuracy. The balanced dataset led to significant improvements, particularly in predicting the flexure-shear failure mode, with accuracy increasing by 6%, recall by 8%, and F1 scores by 7%. The use of the Borderline-SMOTE1 algorithm significantly improved the recognition of samples at failure mode boundaries, enhancing the classification performance of models like k-nearest neighbor and decision tree, which are highly sensitive to data distribution and decision boundaries. This method effectively addressed class imbalance and selected relevant features without requiring complex simulations like traditional methods, proving applicable for discerning failure modes in various concrete members under seismic action.

• Nuclear Engineering and Technology
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• v.56 no.9
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• pp.3750-3757
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• 2024

Rapidly obtaining the coverage characteristics of leaching solution in In-situ Leaching Area of Sandstone Uranium Mines is a necessary condition for optimizing well locations reasonably. In the presented study, the improved algorithm of the Fast Marching Method (FMM) was studied for rapidly solving coverage characteristics to replace the groundwater numerical simulator. First, the effectiveness of the FMM was verified by simulating diffusion characteristics of the leaching solution in In-situ Leaching Area. Second, based on the radial flow pressure equation and the interaction mechanism of the front diffusion of production and injection well flow field, an improved FMM which is suitable for In-situ Leaching Mining, was developed to achieve the co-simulation of production and injection well. Finally, the improved algorithm was applied to engineering practice to guide the design and production. The results show that the improved algorithm can efficiently solve the coverage characteristics of leaching solution, which is consistent with those obtained from traditional numerical simulators. In engineering practice, the improved FMM can be used to rapidly analyze the leaching process, delineate Leaching Blind Spots, and evaluate the rationality of well pattern layout. Furthermore, it can help to achieve iterative optimization and rapid decision-making of production and injection well locations under largescale mining area models.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.4
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• pp.31-41
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• 2016

This paper is to evaluate the performance of a proposed hybrid genetic algorithm (pro-HGA) approach using closed-loop supply chain (CLSC) model. The proposed CLSC model is a integrated supply chain network model both with forward logistics and reverse logistics. In the proposed CLSC model, the reuse, resale and waste disposal using the returned products are taken into consideration. For implementing the proposed CLSC model, two conventional approaches and the pro-HGA are used in numerical experiment and their performances are compared with each other using various measures of performance. The experimental results show that the pro-HGA approach is more efficient in locating optimal solution than the other competing approaches.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3406-3412
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• 2021

A real-time digital time-stamp sorting algorithm used in the In-Beam positron emission tomography (In-Beam PET) is presented. The algorithm is operated in the field programmable gate array (FPGA) and a small amount of registers, MUX and memory cells are used. It is developed for sorting the data of annihilation event from front-end circuits, so as to identify the coincidence events efficiently in a large amount of data. In the In-Beam PET, each annihilation event is detected by the detector array and digitized by the analog to digital converter (ADC) in Data Acquisition Unit (DAQU), with a resolution of 14 bits and sampling rate of 50 MS/s. Test and preliminary operation have been implemented, it can perform a sorting operation under the event count rate up to 1 MHz per channel, and support four channels in total, count rate up to 4 MHz. The performance of this algorithm has been verified by pulse generator and ²²Na radiation source, which can sort the events with chaotic order into chronological order completely. The application of this algorithm provides not only an efficient solution for selection of coincidence events, but also a design of electronic circuit with a small-scale structure.