Huanhuan Nie

Tsinghua University

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Changjiang Zhang

Taizhou University

Shaowei Huang

Tsinghua University

Ying Chen

Transport for London

Mingchao Fang

Zhejiang Normal University

Ying Chen

Harbin Institute of Technology

Zhe Jiang

Yanbian University Hospital

Yunqi Yan

Tsinghua University

Bingqian Liu

Sun Yat-sen University

Yankan Song

Tsinghua Sichuan Energy Internet Research Institute

Yue Xia

China Agricultural University

Cooperative Institutions

Tsinghua University

Zhejiang Normal University

Harbin Institute of Technology

Southeast University

Guangdong University of Technology

China Telecom

Tsinghua Sichuan Energy Internet Research Institute

Fujian Electric Power Survey & Design Institute

Dahua Technology (China)

Rensselaer Polytechnic Institute

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Mammography Classification Based on Convolutional Neural Network

Changjiang Zhang Huanhuan Nie

Limited by various conditions, the features of mammography images are difficult to extract, so it is hard to classify them.The paper proposed a method based on deep learning method to classify benign and malignant mammography images.The convolutional neural network concludes four convolution layers, four pool layers, and two full-connection layers, and a Softmax layer.The paper designed a new network architecture to improve the traditional one.As a result, we have done an experiment on the DDSM database.Compared with other classification methods, it shows that the method proposed in this paper is more effective than other methods.

10.2991/caai-18.2018.35

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Brain Tumor Segmentation Using Fully Convolutional Networks from Magnetic Resonance Imaging

Journal of Medical Imaging and Health Informatics (2018)

Changjiang Zhang Mingchao Fang Huanhuan Nie

10.1166/jmihi.2018.2502

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Citations (4)

An adaptive enhancement method for breast X-ray images based on the nonsubsampled contourlet transform domain and whale optimization algorithm

Medical & Biological Engineering & Computing (2019)

Changjiang Zhang Huanhuan Nie

Contourlet

10.1007/s11517-019-02022-w

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Citations (11)

Optimizing the Post-Disaster Control of Islanded Microgrid: A Multi-Agent Deep Reinforcement Learning Approach

IEEE Access (2020)

Huanhuan Nie Ying Chen Yue Xia Shaowei Huang Bingqian Liu

Extreme disasters may cause the power supply to the distribution system (DS) to be interrupted. The DS is forced to operate in island mode and forms an islanded microgrid (MG). In order to improve the post-disaster resilience of the DS and to provide longer power supply for as many loads as possible with limited generation resources, this paper proposes a multi-agent deep reinforcement learning (DRL) method which realizes a dual control on the source and load sides of the MG. The problem of resilience improvement is converted to a sequential decision making problem, where the objective is to maximize the cumulative MG utility value over the power outage duration. A multi-agent DRL model is proposed to solve the sequential decision making problem. A dual control policy including energy storage management and load shedding strategy is put forward to maximize the utility value of the MG. A reinforcement learning (RL) environment based on OpenAI and OpenDSS for islanded MG is constructed as a simulator, which has a general interface compatible with, and also can be published to, OpenAI Gym. Numerical simulations are performed for an MG equipped with wind turbines, diesel generators, and storage devices to validate the effectiveness of the proposed method. The influences of available generation resources and power outage duration on the control policy are discussed, which validates the strong adaptability of the proposed method in different conditions.

Microgrid

Resilience

Adaptability

Q-learning

10.1109/access.2020.3018142

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Citations (37)

A General Real-time OPF Algorithm Using DDPG with Multiple Simulation Platforms

Huanhuan Nie Ying Chen Yankan Song Shaowei Huang

OPF, as the most basic optimization problem in power system, has been studied for many years, while it's still difficult to obtain a general real-time OPF solution in existed algorithms. In this paper, the application of DRL algorithm DDPG is described for developing a general real-time OPF solution. Facing the curse of dimensionality which is common in application of RL algorithm, we solve the high-dimension or continuous state, action spaces problem respectively using Deep Q-network and Deterministic Policy Gradient. We implement DDPG algorithm successfully in IEEE 9-bus system using two simulation platforms MATPOWER and CloudPSS, and the results are compared with those of MATPOWER built-in OPF algorithm. With MATPOWER, we achieve a general real-time OPF algorithm, which has the same effect to non-real time MATPOWER built-in OPF algorithm. DDPG algorithm is also applied on CloudPSS to solve the OPF problem, which combines the advantages of reinforcement learning and cloud simulation.

10.1109/isgt-asia.2019.8881174

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Citations (11)

Extensive-form Game-Based Pre-Disaster Resilience Planning Considering Post-Disaster Restoration Decisions

Frontiers in Energy Research (2022)

Ying Chen Yunqi Yan Huanhuan Nie Zhe Jiang Ning Zhou

In recent years, extreme natural disasters have occurred frequently, causing a huge impact on the power grid. In this paper, the method of purchasing and deployment of resources of the distribution network is studied to improve the resilience of the system from the perspective of pre-disaster defense. Considering the sequential game relationship among resource providers, utilities, and extreme disasters, this paper establishes a multi-stage extensive-form game model. To realize collaborative pre- and post-disaster defense of the system, this model comprehensively considers pre-disaster resource deployment, post-disaster reconfiguration and repair crew dispatch. The efficient deriving method of non-inferior solution is designed stage by stage, and the backward induction method is adopted to solve the overall game model efficiently. The experimental results under multiple failure scenarios show the proposed method can give a near-optimal pre-disaster resources purchase and deployment plan that takes into account the characteristics of post-disaster recovery, effectively reducing the expected power loss of the distribution system.

Resilience

Disaster Recovery

Control reconfiguration

10.3389/fenrg.2022.889437

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