|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Immune Network-Based Swarm Intelligence and its Applica- tion to Unmanned Aerial Vehicle (UAV) Swarm Co- ordination显示文摘 | Liguo Weng Qingshan Liu Min Xia | 2014 | Neurocomputing2014，125，： | 1 |
| 2 | Fashion retai- ling forecasting based on extreme learning machine with adap- tive metrics of inputs 显示文摘 | XIA Min ZHANG Yingchao WENG Liguo | 2012 | Konwledge-Based System2012，36，： | 1 |
| 3 | Fashion retailing forcasting based on extreme learning machine with adaptive metrics of inputs显示文摘 | Min Xia Yingchao Zhang Liguo Weng | 2012 | Knoledge-Based Systems2012，36，： | 1 |
| 4 | A mobility compensation method for drones in SG-eloT显示文摘In order to achieve the specific goal of a smart grid,the concept of electricity Internet of Things(eloT)has been proposed to assist the monitoring and inspection of power transmission line state and optimize the asset utilization.The long power transmission line and the complex field operation environment urge the introduction of drones into the eloT for fast power transmission line inspection,data collection from sensors for further big data analysis,adaptive control of power line voltage,etc.Additionally,drones can also act as a central communication control or relay point to serve the data exchange among sensors,drones and power transmission line maintenance personnel in the scenario where the conventional mobile communication service is not available.However,the fast mobility of drones may affect the signal transmission and position estimation performance,which may further deteriorate the networking performance.In order to solve this problem,a mobility compensation method is proposed,which includes the steps of frequency offset estimation and relative velocity calculation.Through the Monte Carlo simulations,the proposed algorithm shows favorable gains compared with the conventional ones. | Liguo Weng Yanghui Zhang Yong Yang Min Fang Zelong Yu | 2021 | Digital Communications and Networks2021，7，2： | 0 |
| 5 | STPGTN-AMulti-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data显示文摘Transmission line(TL)Parameter Identification(PI)method plays an essential role in the transmission system.The existing PI methods usually have two limitations:(1)These methods only model for single TL,and can not consider the topology connection of multiple branches for simultaneous identification.(2)Transient bad data is ignored by methods,and the random selection of terminal section data may cause the distortion of PI and have serious consequences.Therefore,a multi-task PI model considering multiple TLs’spatial constraints and massive electrical section data is proposed in this paper.The Graph Attention Network module is used to draw a single TL into a node and calculate its influence coefficient in the transmission network.Multi-Task strategy of Hard Parameter Sharing is used to identify the conductance ofmultiple branches simultaneously.Experiments show that themethod has good accuracy and robustness.Due to the consideration of spatial constraints,the method can also obtain more accurate conductance values under different training and testing conditions. | Shuai Zhang Liguo Weng | 2023 | Computer Modeling in Engineering & Sciences2023，，9： | 0 |
| 6 | Sequence memory based on an oscillatory neural network显示文摘In the brain,the discrete elements in a temporal order is encoded as a sequence memory.At the neural level,the reproducible sequence order of neural activity is very crucial for many cases.In this paper,a mechanism for oscillation in the network has been proposed to realize the sequence memory.The mechanism for oscillation in the network that cooperates with hetero-association can help the network oscillate between the stored patterns,leading to the sequence memory.Due to the oscillatory mechanism,the firing history will not be sampled,the stability of the sequence is increased,and the evolvement of neurons’states only depends on the current states.The simulation results show that neural network can effectively achieve sequence memory with our proposed model. | XIA Min WENG LiGuo WANG ZhiJie FANG JianAn | 2014 | Science China(Information Sciences)2014，57，7： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员