The China-Nepal Highway is a vital land route in the Kush-Himalayan region. The occurrence of mountain hazards in this area is a matter of serious concern. Thus, it is of great importance to perform hazard assessments in a more accurate and real-time way. Based on temporal and spatial sensor data, this study tries to use data-driven algorithms to predict landslide susceptibility. Ten landslide instability factors were prepared, including elevation, slope angle, slope aspect, plan curvature, vegetation index, built-up index, stream power, lithology, precipitation intensity, and cumulative precipitation index. Four machine learning algorithms, namely decision tree (DT), support vector machines (SVM), Back Propagation neural network (BPNN), and Long Short Term Memory (LSTM) are implemented, and their final prediction accuracies are compared. The experimental results showed that the prediction accuracies of BPNN, SVM, DT, and LSTM in the test areas are 62.0%, 72.9%, 60.4%, and 81.2%, respectively. LSTM outperformed the other three models due to its capability to learn time series with long temporal dependencies. It indicates that the dynamic change course of geological and geographic parameters is an important indicator in reflecting landslide susceptibility.
Cloud computing technologies have enabled a new paradigm for advanced product development powered by the provision and subscription of computational services in a multi-tenant distributed simulation environment. The description of computational resources and their optimal allocation among tenants with different requirements holds the key to implementing effective software systems for such a paradigm. To address this issue, a systematic framework for monitoring, analyzing and improving system performance is proposed in this research. Specifically, a radial basis function neural network is established to transform simulation tasks with abstract descriptions into specific resource requirements in terms of their quantities and qualities. Additionally, a novel mathematical model is constructed to represent the complex resource allocation process in a multi-tenant computing environment by considering priority-based tenant satisfaction, total computational cost and multi-level load balance. To achieve optimal resource allocation, an improved multi-objective genetic algorithm is proposed based on the elitist archive and the K-means approaches. As demonstrated in a case study, the proposed framework and methods can effectively support the cloud simulation paradigm and efficiently meet tenants' computational requirements in a distributed environment.
Product assembly is an important stage in complex product manufacturing. How to intelligently plan the assembly process based on dynamic product and environment information has become an pressing issue needs to be addressed. For this reason, this research has constructed a digital twin assembly system, including virtual and real interactive feedback, data fusion analysis and decision-making iterative optimization modules. In the virtual space, a modified Q-learning algorithm is proposed to solve the path planning problem in product assembly. The proposed algorithm speeds up the convergence speed by adding dynamic reward function, optimizes the initial Q table by introducing knowledge and experience through the case-based reasoning (CBR) algorithm, and prevents entry into the trapped area through the obstacle avoiding method. Finally, take the six-joint robot UR10 as an example to verify the performance of the algorithm in the three-dimensional pathfinding space. The experimental resultsshow that the modified Q-learning algorithm's pathfinding performance is significantly better than the original Q-learning algorithm.
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