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Under ideal condition, there are lots of Shortest Path Algorithms such as Dijkstra, Floyd, Johnson algorithms. However, putting these algorithms into use is not as we expected. The reasons cause the problem may be in the Returning Problem mentioned in the following paper. Shortest Path Algorithms only focus on the shortest path between the points, without paying attention to figure out the direct solution from the Starting Point to the Passing Points and finally to the Starting Point. A Shortest Path Algorithm applied in actual practice will be introduced in here, then another case will be introduced and analyzed to explain the following Algorithm: Under the Constraints of time and limit of Loading capacity, figure out the Shortest Path Algorithm through the Starting Point to the Passing Points and back to the Starting Point. At last , the algorithm is of feasibility and practicability.

Hydraulic fracturing is an effective technology used to stimulate hydrocarbon production from unconventional reservoirs. Numerical simulation of the hydraulic fracturing process plays a key role in the design of hydraulic fracturing. However, most of existing models are overly simplified by neglecting the poroelasticity of rock matrix, which may have a substantial effect on the growth of hydraulic fractures, especially in oil and/or water saturated formations. In this study, we developed a fully coupled finite element model for hydraulic fracture propagation in permeable formations by combining XFEM technique with cohesive zone model, and used it to investigate the effects of poroelasticity on the geometry evolution of single fracture which initiated from an injection point. Fluid flow within the fractures, Darcy flow within the rock matrix, hydraulic fractures propagation, and fluid leak-off into the formation are simultaneously taking into account in the model. Then, the model is validated by comparing the results with available analytical solutions. To understand and quantify the poroelastic effects on the propagation of hydraulic fracture, several cases with different matrix permeability, leak-off coefficients, and bulk modulus of pore fluid are performed. The simulation results show that the total volume of leakage is controlled by the combined action of matrix permeability and leak-off coefficient. The fracture aperture and length decrease with the increase of matrix permeability or leak-off coefficient, while as the bulk moduli of pore fluid increases, the fracture aperture and length tend to increase.

According to rapid assessment requirement of system protection control strategy adaptability in future operation mode, combining with application status of distributed computing for power system security and stability analysis, based on design idea of asynchronism and concurrency, this paper proposes a multi-scheme concurrent computing framework suitable for coarse-grained case parallelism. By the way of multi-data section parallel, the asynchronous and concurrent scheduling management of computing task of multiple application functions under different sections is realized, which effectively improves the utilization rate of computing resources and significantly reduces the computing period of advance evaluation of system protection control strategy adaptability. The technology has been applied in provincial power grid and its effectiveness has been verified.

Nowadays, the application of virtual simulation experiment teaching resources in universities is not systematic and comprehensive enough. Therefore, integrating professional-related virtual simulation experiment teaching resources and constructing virtual simulation comprehensive training courses can not only strengthen and expand professional knowledge and improve professional thinking but also strengthen students’ ability to realize innovative thinking. While enriching experimental teaching forms and teaching effects, virtual simulation technology also promotes the integration of experimental teaching and theoretical teaching. The article mainly discusses the application of virtual simulation technology in university chemistry teaching practice and its impact on teaching effects and then summarizes the entry point and expansion form of virtual simulation technology in the curriculum, and serves the promotion of digital and information technology in chemistry teaching application.

Permanent magnet synchronous generators (PMSGs) with full converters have been widely used in wind power generation due to its superior flexibility and controllability. However, under some circumstance, the oscillation modes of PMSG (POMs) may excite strong resonance with the electromechanical oscillation modes (EOMs) of the power system that degrades the power system small signal stability. In this paper, A two-open-loop subsystem model is firstly derived to analyze the oscillation modes. Then the POMs are investigated with modal analysis, the relationship between POMs and related controllers are clarified. On this basis, the strong resonance between PMSG and the external power system is revealed and identified. Furthermore, a five-step parameter tuning method is proposed to relocate the position of POM as well as suppress the strong resonance. Both modal analysis and time-domain simulations validate the effectiveness of the proposed method.

China's business index of macro-economic includes early warning index, coincidence index, leading index and lagging index, among which early warning index reflects the economic running state. However, obtaining these indexes is a complex and daunting task. To simplify the task, this article mainly explores how to use machine learning algorithms including multiple linear regression (MLR), support vector machine regression (SVM), random forest (RF), artificial neural network (ANN) and extreme learning machine (ELM) to accurately predict early warning index. Finally, it can be found that the warning index can be well predicted by above machine learning algorithms with coincidence index, leading index and lagging index to be variables, furthermore, extreme learning machine and random forest are superior to other methods.

In the modern society, intelligent transportation builds on the basic of real-time wireless communication with vast information. Only by adding and upgrading the high-costed equipment to keep the speed of communication with the development of the transportation is far from enough [1]. Thus, to make full use of the present equipment to achieve higher speed of information transmission is a practical and challenging project. The Routing Algorithm of this paper is taking the Traffic Management Center as the center. Starting from the center and covering all the traffic management station, we can get the best path program under multi-limits with the help of the combination Algorithm of Shortest Path Algorithms and Concentric Excursions. A lot of constraints has been taken into consideration so as to make the result preciser. The result of multiple experiments has testified the feasibility and efficiency of the algorithm even under the great information content.

Teaching-Learning-Based Optimization (TLBO) is a new swarm intelligence optimization algorithm that simulates the class learning process. According to such problems of the traditional TLBO as low optimizing efficiency and poor stability, this paper proposes an improved TLBO algorithm mainly by introducing the elite thought in TLBO and adopting different inertia weight decreasing strategies for elite and ordinary individuals of the teacher stage and the student stage. In this paper, the validity of the improved TLBO is verified by the optimizations of several typical test functions and the SVM optimized by the weighted elitist TLBO is used in the diagnosis and classification of common failure data of the TE chemical process. Compared with the SVM combining other traditional optimizing methods, the SVM optimized by the weighted elitist TLBO has a certain improvement in the accuracy of fault diagnosis and classification.