Jun Lee¹, Hyun Kwon^2,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 2025-2042, 2023, DOI:10.32604/cmc.2023.036051

Abstract A concurrency control mechanism for collaborative work is a key element in a mixed reality environment. However, conventional locking mechanisms restrict potential tasks or the support of non-owners, thus increasing the working time because of waiting to avoid conflicts. Herein, we propose an adaptive concurrency control approach that can reduce conflicts and work time. We classify shared object manipulation in mixed reality into detailed goals and tasks. Then, we model the relationships among goal, task, and ownership. As the collaborative work progresses, the proposed system adapts the different concurrency control mechanisms of shared object manipulation according to the modeling of… More >

Yu Xia¹, Jian Guan⁴, Yong Gao⁵, Shanfa Tang^1,2,3,*, Jialuo Rong¹

FDMP-Fluid Dynamics & Materials Processing, Vol.19, No.6, pp. 1609-1622, 2023, DOI:10.32604/fdmp.2023.025263

Abstract The ash mudstone in some oil formations is highly water-sensitive. The oil formation is fractured, and the risk of well leakage and collapse is not negligible. This study presents a countermeasure for well collapse prevention, based on a “force-chemistry synergistic balance” approach and the utilization of environmentally friendly and efficient hydration inhibitors. The relevance of this approach is demonstrated considering a drilling fluid system with the high potassium content. The analysis shows that the system can maintain good rheological properties, filtration loss and suspension stability even after aging at 130°C for 16 h. The primary roll recovery of rock chips… More >

Xiaowu Cheng, Xinyan Tao, Lu Wang^*

Journal of Renewable Materials, Vol.11, No.2, pp. 905-920, 2023, DOI:10.32604/jrm.2022.023196

Abstract This paper presents experimental and numerical investigations on progressive collapse behavior of a two-story glulam frame when the side column is exposed to ISO834 standard fire. The collapse mechanism initiated by fire is identified. The experimental results show that the progressive collapse of a glulam frame could be described for three stages, namely bending effect stage, catenary effect stage and failure stage, respectively. These stages are discussed in detail to understand the structural behavior before and during collapse. It is demonstrated that the entire frame slopes towards the side of the heated column, and the “overturning” collapse occurs eventually. The… More >

Dahyeon Kim, Junho Ahn^*

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1657-1671, 2023, DOI:10.32604/cmc.2023.029216

Abstract When firefighters search inside a building that is at risk of collapse due to abandonment or disasters such as fire, they use old architectural drawings or a simple monitoring method involving a video device attached to a robot. However, using these methods, the disaster situation inside a building at risk of collapse is difficult to detect and identify. Therefore, we investigate the generation of digital maps for a disaster site to accurately analyze internal situations. In this study, a robot combined with a low-cost camera and two-dimensional light detection and ranging (2D-lidar) traverses across a floor to estimate the location… More >

Xindai An¹, Di Wu^1,2,*, Xiangwen Xie¹, Kefeng Song¹

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1091-1103, 2023, DOI:10.32604/cmes.2022.020670

Abstract So far, slope collapse detection mainly depends on manpower, which has the following drawbacks: (1) low reliability, (2) high risk of human safe, (3) high labor cost. To improve the efficiency and reduce the human investment of slope collapse detection, this paper proposes an intelligent detection method based on deep learning technology for the task. In this method, we first use the deep learning-based image segmentation technology to find the slope area from the captured scene image. Then the foreground motion detection method is used for detecting the motion of the slope area. Finally, we design a lightweight convolutional neural… More >

Chandrakant Dondariya^*, D. K. Sakravdia

Energy Engineering, Vol.119, No.3, pp. 947-963, 2022, DOI:10.32604/ee.2022.019118

Abstract This paper presents a newly developed proximity indicator for voltage stability assessment which can be used to predict critical real system load and voltages at various load buses at critical loading point. The proximity indicator varies almost parabolic with total real load demand and reaches orthogonally to real load axis. This relation has been utilized to predict critical loading point. It has been shown that two operating points are needed for estimating critical point and proper selection of operating points and variation of proximity indicator near collapse point highly affect the accuracy of estimation. Simulation is based on load flow… More >

Zele Li¹, Mohammad Noori², Wael A. Altabey^1,3,*

Structural Durability & Health Monitoring, Vol.15, No.2, pp. 103-123, 2021, DOI:10.32604/sdhm.2021.011193

Abstract Earth buildings are common types of structures in most rural areas in all developing countries. Catastrophic failure and destruction of these structures under seismic loads always result in loss of human lives and economic losses. Wall is an important load-bearing component of raw soil buildings. In this paper, a novel approach is proposed to improve the strength and ductility of adobe walls. Three types of analyses, material properties, mechanical properties, and dynamic properties, are carried out for the seismic performance assessment of the adobe walls. These performed studies include that, material properties of the earth cylinder block, mechanical properties of… More >

Zhibin Lin¹, Boyang Zhang^1,2,*, Jiaqi Guo¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 311-330, 2021, DOI:10.32604/cmes.2021.011556

Abstract Minin-induced water inrush from a confined aquifer due to subsided floor karst collapse column (SKCC) is a type of serious disaster in the underground coal extraction. Karst collapse column (KCC) developed in a confined aquifer occurs widely throughout northern China. A water inrush disaster from SKCC occurred in Taoyuan coal mine on February 3, 2013. In order to analyze the effect of the KCC influence zone’s (KCCIZ) width and the entry driving distance of the water inrush through the fractured channels of the SKCC, the stress, seepage, and impact dynamics coupling equations were used to model the seepage rule, and… More >

Hongtao Wang^1,2,*, Ping Liu^1,2, Lige Wang^3,4,*, Chi Liu⁵, Xin Zhang^1,2, Luyao Liu^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.1, pp. 375-391, 2020, DOI:10.32604/cmes.2020.08270

Abstract Layered rock strata are observed to be common during the excavation of tunnels or cavities, and may significantly affect the deformation and failure characteristics of surrounding rock masses due to various complex forms and mechanical properties. In this paper, we propose a three-dimensional axisymmetric velocity field for roof collapse of shallow cavities in multi rock layers, by considering the influences of roof cross-section shapes, supporting pressure, ground overload, etc. The internal energy dissipation rate and work rates of external forces corresponding to the velocity field are computed by employing the Hoek-Brown strength criterion and its associated flow rule. Further, the… More >

Tong Li¹, Shibin Zhang^{1, *}, Jinyue Xia²

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 401-438, 2020, DOI:10.32604/cmc.2020.010551

Abstract Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren… More >