中国科学院机构知识库网格系统: 检索

DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization 期刊论文 OAI收割
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 页码: 14
作者: Huang, Nisha; Zhang, Yuxin; Tang, Fan; Ma, Chongyang; Huang, Haibin

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收藏 | 浏览/下载：15/0 | 提交时间：2024/05/20

Arbitrary image stylization diffusion textual guidance neural network applications

DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization 期刊论文 OAI收割
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 页码: 14
作者: Huang, Nisha; Zhang, Yuxin; Tang, Fan; Ma, Chongyang; Huang, Haibin

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收藏 | 浏览/下载：23/0 | 提交时间：2024/07/03

Arbitrary image stylization diffusion textual guidance neural network applications

Domain-Aware Graph Network for Bridging Multi-Source Domain Adaptation 期刊论文 OAI收割
IEEE TRANSACTIONS ON MULTIMEDIA, 2024, 卷号: 26, 页码: 7210-7224
作者: Yuan, Jin; Hou, Feng; Yang, Ying; Zhang, Yang; Shi, Zhongchao

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收藏 | 浏览/下载：7/0 | 提交时间：2024/12/06

Task analysis Feature extraction Graph neural networks Adaptation models Self-supervised learning Multitasking Image color analysis Multi-source domain adaptation self-supervised learning graph neural network real-world applications

Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm SCI/SSCI论文 OAI收割
2016
作者: Creaco E.; Berardi, L.; Sun, S. A.; Giustolisi, O.; Savic, D.

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收藏 | 浏览/下载：31/0 | 提交时间：2017/11/09

calibration data selection neural-network models resources applications turbidity measurements sensitivity performance algorithms prediction loads epr

Selection of relevant input variables in storm water quality modeling by multiobjective evolutionary polynomial regression paradigm SCI/SSCI论文 OAI收割
2016
作者: Creaco E.; Berardi, L.; Sun, S. A.; Giustolisi, O.; Savic, D.

收藏 | 浏览/下载：29/0 | 提交时间：2016/12/16

calibration data selection neural-network models resources applications turbidity measurements sensitivity performance algorithms prediction loads epr

A Survey on CPG-Inspired Control Models and System Implementation 期刊论文 OAI收割
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 卷号: 25, 期号: 3, 页码: 441-456
作者: Yu, Junzhi; Tan, Min; Chen, Jian; Zhang, Jianwei

收藏 | 浏览/下载：56/0 | 提交时间：2015/08/12

Bioinspired control central pattern generator (CPG) neural network parameter tuning robotic applications

A survey on CPG-inspired control models and system implementation 期刊论文 OAI收割
IEEE Transactions on Neural Networks and Learning Systems, 2014, 卷号: 25, 期号: 3, 页码: 441-456
作者: Yu, Junzhi ; Tan, Min ; Chen, Jian ; Zhang, Jianwei ; Cheng, Long

收藏 | 浏览/下载：24/0 | 提交时间：2017/01/23

robotic applications Bioinspired control central pattern generator (CPG) neural network parameter tuning

Multi-focus image fusion algorithm based on adaptive PCNN and wavelet transform (EI CONFERENCE) 会议论文 OAI收割
International Symposium on Photoelectronic Detection and Imaging 2011: Advances in Imaging Detectors and Applications, May 24, 2011 - May 26, 2011, Beijing, China
Wu Z.-G.; Wang M.-J.; Han G.-L.

收藏 | 浏览/下载：78/0 | 提交时间：2013/03/25

Being an efficient method of information fusion image fusion has been used in many fields such as machine vision medical diagnosis military applications and remote sensing.In this paper Pulse Coupled Neural Network (PCNN) is introduced in this research field for its interesting properties in image processing including segmentation target recognition et al. and a novel algorithm based on PCNN and Wavelet Transform for Multi-focus image fusion is proposed. First the two original images are decomposed by wavelet transform. Then based on the PCNN a fusion rule in the Wavelet domain is given. This algorithm uses the wavelet coefficient in each frequency domain as the linking strength so that its value can be chosen adaptively. Wavelet coefficients map to the range of image gray-scale. The output threshold function attenuates to minimum gray over time. Then all pixels of image get the ignition. So the output of PCNN in each iteration time is ignition wavelet coefficients of threshold strength in different time. At this moment the sequences of ignition of wavelet coefficients represent ignition timing of each neuron. The ignition timing of PCNN in each neuron is mapped to corresponding image gray-scale range which is a picture of ignition timing mapping. Then it can judge the targets in the neuron are obvious features or not obvious. The fusion coefficients are decided by the compare-selection operator with the firing time gradient maps and the fusion image is reconstructed by wavelet inverse transform. Furthermore by this algorithm the threshold adjusting constant is estimated by appointed iteration number. Furthermore In order to sufficient reflect order of the firing time the threshold adjusting constant is estimated by appointed iteration number. So after the iteration achieved each of the wavelet coefficient is activated. In order to verify the effectiveness of proposed rules the experiments upon Multi-focus image are done. Moreover comparative results of evaluating fusion quality are listed. The experimental results show that the method can effectively enhance the edge details and improve the spatial resolution of the image. 2011 SPIE.

Neural network based online traffic signal controller design with reinforcement training (EI CONFERENCE) 会议论文 OAI收割
14th IEEE International Intelligent Transportation Systems Conference, ITSC 2011, October 5, 2011 - October 7, 2011, Washington, DC, United states
Dai Y.; Hu J.; Zhao D.; Zhu F.

收藏 | 浏览/下载：29/0 | 提交时间：2013/03/25

Traffic congestion leads to problems like delays decreasing flow rate and higher fuel consumption. Consequently keeping traffic moving as efficiently as possible is not only important to economy but also important to environment. Traffic system is a large complex nonlinear stochastic system. Traditional mathematical methods have some limitations when they are applied in traffic control. Thus computational intelligence (CI) technologies gain more and more attentions. Neural Networks (NNs) is a well developed CI technology with lots of promising applications in traffic signal control (TSC). In this paper a neural network (NN) based signal controller is designed to control the traffic lights in an urban traffic road network. Scenarios of simulation are conducted under a microscopic traffic simulation software. Several criterions are collected. Results demonstrate that through online reinforcement training the controllers obtain better control effects than the widely used pre-time and actuated methods under various traffic conditions. 2011 IEEE.

Predictions of HF communication MUF in the region of the South China Sea 期刊论文 OAI收割
IEEE ANTENNAS AND PROPAGATION MAGAZINE, 1999, 卷号: 41, 期号: 4, 页码: 35-38
作者: Zeng, W; Zhang, XJ

收藏 | 浏览/下载：24/0 | 提交时间：2015/12/01

neural network applications MUF HF radio propagation prediction methods South China Sea