Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 2096-3246) is a bi-monthly peer-reviewed international Journal. Gongcheng Kexue Yu Jishu/Advanced Engineering Science was originally formed in 1969 and the journal came under scopus by 2017 to now. The journal is published by editorial department of Journal of Sichuan University. We publish every scope of engineering, Mathematics, physics.
Gongcheng Kexue Yu Jishu/Advanced Engineering Science (ISSN: 20963246) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to:
In heterogeneous MPSoC,the parallel tasks were dispatched to each cores by task scheduling algorithm.Therefore,the performance of heterogeneous MPSoC directly was affected by task scheduling algorithm.A novel task scheduling algorithm with load-adaptive capability was proposed.In order to reduce the communication overheads,the algorithm divided task-set into task-subsets based on core types and tasks dependencies.Taking into the account of the cores load,then weighted bipartite graph was then created by task-subsets,cores and the execution efficiency of the task-subsets on each core.Finally,task-subsets were dispatched to appropriate load cores by finding a maximum weight matching in the weighted bipartite graph.In this way,the average scheduling length of task-set was reduced and the utilization of cores was improved,which jointly improved the performance of the heterogeneous MPSoC.Under the simulation scenarios with different number of tasks,maximum number of predecessors,number of core types,and number of cores,the proposed algorithm was quantitatively analyzed in terms of the average scheduling length of task-set and the utilization of the cores.The results showed that the proposed algorithm could effectively reduce the average scheduling length of task-set,and improved the utilization of heterogeneous MPSoC cores while achieving-adaptive loading..
With the increasing operation of hydropower stations, a refined generation dispatch model becomes more urgently needed for the precise control of cascade hydropower stations under deterministic runoff, such as rules extraction by simulation operation under long series runoff, optimal dispatch level evaluation of historical operation process, and etc. However, most of the studies on refined generation dispatch model focused on the refined description of transform relationships between hydropower and electric energy or hydraulic connection between cascade hydropower stations. The time scale of dispatch model, which is another important factor affecting the accuracy of dispatch model, was short of attention. The time scale of dispatch model affects theaccuracy of dispatch model in a complex way. This paper attempted to discover the influence between time scale and the accuracy of dispatch model by both theoretical analysis and case study. The control variable method and progressive analytical method were used to uncover the key factors which affect the accuracy of dispatch model and to analyze its influence rule qualitatively. The case study of cascade hydropower stations downstream of the Jinsha River has been conducted to analyze influence between time scale and the accuracy of dispatch model by these factors. With modeling analysis in different inflow conditions and water level control strategies, we have uncovered key factors which affect the accuracy of dispatch model quantitatively. Then we proposed a multiple time-scale generation dispatch model for accurate description of the dispatch problem. The simulation results show that the theoretic results proposed in this paper is sound. Moreover, the proposed model has a better performance on both accuracy and efficiency compared with other models, which means that this model can be used for historical operation evaluation and dispatch rules extraction of cascade hydropower stations downstream of the Jinsha River..
Human mobility has regularity.It has theoretical significance and realistic needs to build mobility models of human movement.Previous models were usually based on the assumption that human mobility is scalable and did not take into account the times of trips and influence of infrastructure network.To address these problems,by analyzing passengers' traveling data sets of civil aviation,the following characteristics of passengers' traveling were found:the trip distances of passengers are not scale-free,the trip number of times is anisotropic,and passengers' movements have different trends along with different numbers of trip times.In order to describe the characteristics of passengers' travelling data,a passenger mobility model based on potential trip purpose (PMMPTP) was proposed.Firstly,the model considered that passengers' travel has relationship with the economic factors of destination cities,and a method for calculating the selection probability of a city was proposed.Secondly,the exploration and return mechanism of passengers' travelling was taken into account.Then,a method for calculating the probability that passengers explore new airports and return to the historic airports was proposed.The simulation experiments firstly simulated the travel characteristics of civil aviation using PMMPTP model and then tested the validity of the model by predicting the throughput of airport and airline.The results showed that the model can fit the actual data of civil aviation travel,and effectively solve the problem of modeling low frequency passenger's trips..
For researching the rockburst proneness of Beishan granite under different stress state, servo Test System was used in this study. The stress-strain curves of Beishan granite under different confining pressures were obtained, and the loading process was controlled by axial pressures and circumferential deformation. It was mainly focused on the energy evolution of rocks in the process of failure, and the results proved that both the total energy and elastic energy increased with increasing confining pressure when rock failure. Variable quantity of total energy and elastic energy from peak stress to residual stress increased with increasing confining pressure. The effect of the energy storage coefficient and the energy releasing coefficient on the rock failue were analysed, and a new rock burst proneness index was performed based on the energy storage and releasing coefficient, which not only represented energy change characteristics but also expresses the rockburst proneness under different stress states. It was thusused to research the rockburst proneness under different stress states, and the result showed that the rockburst proneness increased with increasing confining pressure. Specifically, when the confining was less than 10 MPa, the effect of confining on rockburst proneness was weakened;when the confining pressure exceeded 10 MPa, the effect on rockburst proneness sharply increased;thereafter, as the confining continues to increase, the effect was gradually decreased..
We study sensor networks with energy harvesting nodes. The generated energy at a node can be stored in a buffer. A sensor node periodically senses a random field and generates a packet. These packets are stored in a queue and transmitted using the energy available at that time at the node. For such networks we develop efficient energy management policies. First, for a single node, we obtain policies that are throughput optimal, i.e., the data queue stays stable for the largest possible data rate. Next we obtain energy management policies which minimize the mean delay in the queue. We also compare performance of several easily implementable suboptimal policies. A greedy policy is identified which, in low SNR regime, is throughput optimal and also minimizes mean delay. Next using the results for a single node, we develop efficient MAC policies..