• Wind and Structures
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• v.13 no.1
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• pp.57-82
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• 2010

The 486m-long roof of Shenzhen Citizens Centre is one of the world's longest spatial lattice roof structures. A comprehensive numerical study of wind effects on the long-span structure is presented in this paper. The discretizing and synthesizing of random flow generation technique (DSRFG) recently proposed by two of the authors (Huang and Li 2008) was adopted to produce a spatially correlated turbulent inflow field for the simulation study. The distributions and characteristics of wind loads on the roof were numerically evaluated by Computational Fluid Dynamics (CFD) methods, in which Large Eddy Simulation (LES) and Reynolds Averaged Navier-Stokes Equations (RANS) Model were employed. The main objective of this study is to explore a useful approach for estimations of wind effects on complex curved roof by CFD techniques. In parallel with the numerical investigation, simultaneous pressure measurements on the entire roof were made in a boundary layer wind tunnel to determine mean, fluctuating and peak pressure coefficient distributions, and spectra, spatial correlation coefficients and probability characteristics of pressure fluctuations. Numerical results were then compared with these experimentally determined data for validating the numerical methods. The comparative study demonstrated that the LES integrated with the DSRFG technique could provide satisfactory prediction of wind effects on the long-span roof with complex shape, especially on separation zones along leading eaves where the worst negative wind-induced pressures commonly occur. The recommended LES and inflow turbulence generation technique as well as associated numerical treatments are useful for structural engineers to assess wind effects on a long-span roof at its design stage.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.173-184
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• 2018

In medical or public health research, it is common to encounter clustered or longitudinal count data that exhibit excess zeros. For example, health care utilization data often have a multi-modal distribution with excess zeroes as well as a multilevel structure where patients are nested within physicians and hospitals. To analyze this type of data, zero-inflated count models with mixed effects have been developed where a count response variable is assumed to be distributed as a mixture of a Poisson or negative binomial and a distribution with a point mass of zeros that include random effects. However, no study has considered a situation where data are also censored due to the finite nature of the observation period or follow-up. In this paper, we present a weighted version of zero-inflated Poisson model with random effects accounting for variable individual follow-up times. We suggested two different types of weight function. The performance of the proposed model is evaluated and compared to a standard zero-inflated mixed model through simulation studies. This approach is then applied to Medicaid data analysis.

• Ocean Systems Engineering
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• v.3 no.2
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• pp.97-122
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• 2013

For the optimization design and strength evaluation of the umbilical cable, the calculation of cross section stress is of great importance and very time consuming. To calculate the cross section stress under combined tension and bending loads, a new integrated analytical model of umbilical cable is presented in this paper. Based on the Hook's law, the axial strain of helical components serves as the tensile stress. Considering the effects of friction between helical components, the bending stress is divided into elastic bending stress and friction stress. For the former, the elastic bending stress, the curvature of helical components is deduced; and for the latter, the shear stress before and after the slipping of helical components is determined. This new analytical model is validated by the experimental results of an umbilical cable. Further, this model is applied to estimate the extreme strength and fatigue life of the umbilical cable used in South China Sea.

• Geomechanics and Engineering
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• v.38 no.4
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• pp.367-380
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• 2024

In this study, a prediction model for the cutting force evolution in brittle rocks was developed. This model is based on indentation tests using a conical pick at a cutting depth of 9 mm. The behavior of the indentation mechanism was analyzed in three phases by using Evans' cutting mode. The peak values in the force history identified these phases. The variation in the local strength of the rock caused a large offset in the model prediction of chipping. Regression analyses showed that there is a strong power relationship between the upper bound of the cutting force along with chipping and depth of cut. The slope of the three crushing phases has been found to increase sequentially (α₁<α₂<α₃). In addition, a positive correlation existed between the Schmidt hardness and brittleness index that affects the lower and upper bounds of chipping. Consequently, the results clearly demonstrate that the new model can reasonably predict the evolution of the cutting force based on experimental data. These results would be beneficial for engineers to design and select the optimum excavation machine to reduce mechanical vibration and enhance cutting efficiency.

• Bulletin of the Korean Chemical Society
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• v.34 no.3
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• pp.777-782
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• 2013

In order to further reduce the sulfur content in liquid hydrocarbon fuels, a desulfurization process by adsorption for removing dimethyl sulfide (DMS) and propylmercaptan (PM) was investigated. Bentonite adsorbents modified by $CuCl_2$ for the desulfurization of model oil was investigated. The results indicated that the modified bentonite adsorbents were effective for adsorption of DMS and PM. The bentonite adsorbents were characterized by X-ray diffraction (XRD) and thermal analysis (TGA). The acidity was measured by FT-IR spectroscopy. Several factors that influence the desulfurization capability, including loading and calcination temperature, were studied. The maximum sulfur adsorption capacity was obtained at a Cu(II) loading of 15 wt %, and the optimum calcination temperature was $150^{\circ}C$. Spectral shifts of the ${\nu}$(C-S) and ${\nu}$(Cu-S) vibrations of the complex compound obtained by the reaction of $CuCl_2$ and DMS were measured with the Raman spectrum. On the basis of complex adsorption reaction and hybrid orbital theory, the adsorption on modified bentonite occurred via multilayer intermolecular forces and S-M (${\sigma}$) bonds.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.9
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• pp.2285-2301
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• 2012

Peer-to-Peer (P2P) networks are becoming more and more popular in video content delivery services, such as Video on Demand (VoD). Scalability feature of P2P allows a higher number of simultaneous users at a given server load and bandwidth to use stream service. However, the quality of service (QoS) in these networks is difficult to be guaranteed because of the free-riding problem that nodes download the recourses while never uploading recourses, which degrades the performance of P2P VoD networks. In this paper, a distributed trust model is designed to reduce node's free-riding phenomenon in P2P VoD networks. In this model, the P2P network is abstracted to be a super node hierarchical structure to monitor the reputation of nodes. In order to calculate the reputation of nodes, the Hidden Markov Model (HMM) is introduced in this paper. Besides, a distinction algorithm is proposed to distinguish the free-riders and malicious nodes. The free-riders are the nodes which have a low frequency to free-ride. And the malicious nodes have a high frequency to free-ride. The distinction algorithm takes different measures to response to the request of these two kinds of free-riders. The simulation results demonstrate that this proposed trust model can improve QoS effectively in P2P VoD networks.

• Industrial Engineering and Management Systems
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• v.10 no.2
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• pp.154-160
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• 2011

In this paper, a reliability sampling plan under progressively type-1 interval censoring is proposed when the lifetime of products follows the Pareto distribution of second kind. We use the maximum likelihood estimator for the median life and its asymptotic distribution. The cost model is proposed and the design parameters are determined such that the given producer's and the consumer's risks are satisfied. Tables are given and the results are explained with examples.

• Nuclear Engineering and Technology
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• v.56 no.8
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• pp.2958-2973
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• 2024

Sliding bearings are crucial rotating mechanical components in nuclear power plants, and their failures can result in severe economic losses and human casualties. Deep learning provides a new approach to bearing fault diagnosis, but there is currently a lack of a universal fault diagnosis model for studying bearing-rotor systems under various operating conditions, speeds and faults. Research on bearing-rotor systems supported by sliding bearings is limited, leading to insufficient fault data. To address these issues, this paper proposes a fault diagnosis model framework for bearing-rotor systems based on a deep convolutional generative adversarial network (TF-DLGAN). This model not only exhibits outstanding fault diagnosis performance but also addresses the issue of insufficient fault data. An experimental platform is constructed to conduct fault experiments under various operating conditions, speeds and faults, establishing a dataset for sliding bearing-rotor system faults. Finally, the model's effectiveness is validated using this dataset.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3904-3922
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• 2022

As a research hotspot, pedestrian detection has a wide range of applications in the field of computer vision in recent years. However, current pedestrian detection methods have problems such as insufficient detection accuracy and large models that are not suitable for large-scale deployment. In view of these problems mentioned above, a lightweight pedestrian detection and early warning method using a new model called you only look once (Yolov5) is proposed in this paper, which utilizing advantages of Yolov5s model to achieve accurate and fast pedestrian recognition. In addition, this paper also optimizes the loss function of the batch normalization (BN) layer. After sparsification, pruning and fine-tuning, got a lot of optimization, the size of the model on the edge of the computing power is lower equipment can be deployed. Finally, from the experimental data presented in this paper, under the training of the road pedestrian dataset that we collected and processed independently, the Yolov5s model has certain advantages in terms of precision and other indicators compared with traditional single shot multiBox detector (SSD) model and fast region-convolutional neural network (Fast R-CNN) model. After pruning and lightweight, the size of training model is greatly reduced without a significant reduction in accuracy, and the final precision reaches 87%, while the model size is reduced to 7,723 KB.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.