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Deep reinforcement learning 1

Finite fields 1

Galois NFSRs 1

Graph-based communication 1

Heterogeneous settings 1

Logical networks 1

Maximum-entropy learning 1

Nonlinear feedback shift registers (NFSRs) 1

Observability 1

Partial observability 1

Semi-tensor product 1

TMA 1

doubly-fed induction generator (DFIG) 1

low-frequency oscillation (LFO) 1

observability 1

observability measure 1

passive location 1

sensitivity 1

wind power system 1

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Improvement to observability measures of LFO modes in power systems with DFIGs

Shenghu LI

Frontiers in Energy 2021, Volume 15, Issue 2,   Pages 539-549 doi: 10.1007/s11708-019-0617-z

Abstract: The definition of the observability measure is improved to consider the initial output and the attenuationThe sensitivities of the observability measures to the control parameters are derived.results from the small and large-disturbance validate the LFO modes caused by the DFIGs, and different observabilityAdjustment of the control parameters is chosen based on the sensitivity model to improve the observability

Keywords： wind power system     low-frequency oscillation (LFO)     observability measure     sensitivity     doubly-fed induction    

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A Survey of the Observability for Single Observer Passive Location

Deng Xinpu

Strategic Study of CAE 2007, Volume 9, Issue 11,   Pages 54-62

Abstract:  This leads to the question of observability. In this paper,  the state-observability problem for passive target tracking by angle measurements The observability for target tracking with frequency measurements is also analyzed. Degree of observability is discussed. And a concise review of papers on observability analysis is presented.

Keywords： passive location     TMA     observability    

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On observability of Galois nonlinear feedback shift registers over finite fields Research Article

Zhe GAO, Jun´e FENG, Yongyuan YU, Yanjun CUI

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 10,   Pages 1533-1545 doi: 10.1631/FITEE.2200228

Abstract:

ensures that any two distinct initial states can be uniquely determined by their outputs, so the stream ciphers can avoid unobservable to prevent the occurrence of equivalent keys. This paper discusses the of over . are treated as using the . The vector form of the state transition matrix is introduced, by which a necessary and sufficient condition is proposed, as well as an algorithm for determining the of general . Moreover, a new matrix is defined, which can derive a matrix method with lower computation complexity. Furthermore, the of two special types of , a full-length Galois NFSR and a nonsingular Galois NFSR, is investigated. Two methods are proposed to determine the of these two special types of NFSRs, and some numerical examples are provided to support these results.

Keywords： Observability     Nonlinear feedback shift registers (NFSRs)     Galois NFSRs     Semi-tensor product     Finite    

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Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments Research Article

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 117-130 doi: 10.1631/FITEE.2200073

Abstract: The recent progress in multi-agent (MADRL) makes it more practical in real-world tasks, but its relatively poor scalability and the partially observable constraint raise more challenges for its performance and deployment. Based on our intuitive observation that human society could be regarded as a large-scale partially observable environment, where everyone has the functions of communicating with neighbors and remembering his/her own experience, we propose a novel network structure called the hierarchical graph recurrent network (HGRN) for multi-agent cooperation under . Specifically, we construct the multi-agent system as a graph, use a novel graph convolution structure to achieve communication between heterogeneous neighboring agents, and adopt a recurrent unit to enable agents to record historical information. To encourage exploration and improve robustness, we design a method that can learn stochastic policies of a configurable target action entropy. Based on the above technologies, we propose a value-based MADRL algorithm called Soft-HGRN and its actor-critic variant called SAC-HGRN. Experimental results based on three homogeneous tasks and one heterogeneous environment not only show that our approach achieves clear improvements compared with four MADRL baselines, but also demonstrate the interpretability, scalability, and transferability of the proposed model.

Keywords： Deep reinforcement learning     Graph-based communication     Maximum-entropy learning     Partial observability    

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Title Author Date Type Operation

Improvement to observability measures of LFO modes in power systems with DFIGs

Shenghu LI

Journal Article

A Survey of the Observability for Single Observer Passive Location

Deng Xinpu

Journal Article

On observability of Galois nonlinear feedback shift registers over finite fields

Zhe GAO, Jun´e FENG, Yongyuan YU, Yanjun CUI

Journal Article

Soft-HGRNs: soft hierarchical graph recurrent networks for multi-agent partially observable environments

Yixiang REN, Zhenhui YE, Yining CHEN, Xiaohong JIANG, Guanghua SONG,yixiangren@zju.edu.cn,zhenhuiye@zju.edu.cn,ch19930611@zju.edu.cn,ghsong@zju.edu.cn

Journal Article