Analysis and comparisons are made for the present single sign-on model in this paper.Combined with the advantages of agent-based and broker-based models,the easy constructed three-level single sign-on model is proposed for the network of small and medium size enterprises.
Deep Reinforcement Learning has shown significant progress in complex sequential decision-making tasks. Due to the uncertainty of the power system and the volatility of new energy sources, conventional scheduling methods may lose their effectiveness. This paper proposes an approach for solving the high-renewable penetrated power system scheduling problem by Proximal Policy Optimization (PPO) algorithm. Since the action in the method is constrained within a certain range, the convergence of the algorithm is better. The IEEE 30-bus system is used to verify the method and compared with the Deep Q-learning (DQN) , Policy Gradient (PG) algorithms, the results show that the method in this paper has better results in dealing with continuous action space problems.
There is growing awareness that metabolic heterogeneity of organism provides vital insight into the disease with molecular mechanism and personalized therapy. The screening of metabolism-related sub-regions that affect disease development is essential for the more focused exploration how disease progress aberrant phenotypes, even carcinogenesis and metastasis. Mass spectrometry imaging (MSI) technique has distinct advantages to reveal the heterogeneity of organism based on the in situ molecular profiles. The challenge of heterogeneous analysis has been to perform an objective identification among biological tissues with different characteristics. By introducing the divide-and-conquer strategy to architecture design and application, we establish here a flexible unsupervised deep learning model, called divide-and-conquer (dc)-DeepMSI, for metabolic heterogeneity analysis from MSI data without prior knowledge of histology. dc-DeepMSI can be used to identify either spatially contiguous region-of-interest (ROIs) or spatially sporadic ROIs. We demonstrate that the novel learning strategy successfully obtain sub-regions that are statistically linked to invasion status and molecular phenotypes of breast cancer, as well as organizing principles during developmental phase.
In this paper, we propose an ultra-high precision planarization process based on ion beam shaping (IBS) to meet the stricter requirements of current advanced process technology node miniaturization and chip 3D development process. Under this high stability process, nanoscale or angstrom level thickness and uniformity control across whole wafer can be achieved. We have recently demonstrated atomic level ultra precision modification capability via ion beam shaping (IBS) technology in multiple scenarios. Some examples include: surface roughness modification, CMP-like polishing, etch back process and loading improvement. Which ultimately achieving 3σ < 15Å within 300mm wafers, meeting the strict intra wafer uniformity goals for Fin-FET or even GAA technology.
Most existing work on sensor network target tracking concentrates on sensor selection solely based upon one characteristics such as the sensor position or sensing modality, and not much work has been done on collecting compositive information. In wireless sensor network target tracking, there are many influencing factors and the dimensions of these factors are different. This article combines the AHP(analytical hierarchy process) and the fuzzy mathematics method fundamental theory and presents a node selection algorithm. The algorithm estimates the possible trajectory of the target and wakes the appropriate node to participate in tracking. The results of the simulation indicate that the algorithm can turn off a mass of nodes and ensure the quality of the tracking simultaneously.
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