The Experts below are selected from a list of 104940 Experts worldwide ranked by ideXlab platform
Dennice F Gayme - One of the best experts on this subject based on the ideXlab platform.
-
input output Inspired Method for permissible perturbation amplitude of transitional wall bounded shear flows
Physical Review E, 2020Co-Authors: Chang Liu, Dennice F GaymeAbstract:The precise set of parameters governing transition to turbulence in wall-bounded shear flows remains an open question; many theoretical bounds have been obtained, but there is not yet a consensus between these bounds and experimental or simulation results. In this work, we focus on a Method to provide a provable Reynolds-number-dependent bound on the amplitude of perturbations a flow can sustain while maintaining the laminar state. Our analysis relies on an input-output approach that partitions the dynamics into a feedback interconnection of the linear and nonlinear dynamics (i.e., a Lur\'e system that represents the nonlinearity as static feedback). We then construct quadratic constraints of the nonlinear term that is restricted by system physics to be energy-conserving (lossless) and to have bounded input-output energy. Computing the region of attraction of the laminar state (set of safe perturbations) and permissible perturbation amplitude are then reformulated as linear matrix inequalities, which allows more computationally efficient solutions than prevailing nonlinear approaches based on the sum of squares programming. The proposed framework can also be used for energy Method computations and linear stability analysis. We apply our approach to low-dimensional nonlinear shear flow models for a range of Reynolds numbers. The results from our analytically derived bounds are consistent with the bounds identified through exhaustive simulations. However, they have the added benefit of being achieved at a much lower computational cost and providing a provable guarantee that a certain level of perturbation is permissible.
Guangyong Zeng - One of the best experts on this subject based on the ideXlab platform.
-
a mussel Inspired Method to fabricate reduced graphene oxide g c3n4 composites membranes for catalytic decomposition and oil in water emulsion separation
Chemical Engineering Journal, 2017Co-Authors: Heng Shi, Yang Pan, Guangyong Zeng, Qi Chen, Qiangbin Yang, Li YanAbstract:Abstract In the study, the reduced graphene oxide/graphitic carbon nitride sheet membrane (RGO/PDA/g-C3N4) was fabricated by the dopamine modification and assembling the RGO/PDA/g-C3N4 composites on the surface of commercial CA (cellulose acetate) membrane, consisting of an RGO/PDA/g-C3N4-CA composites membrane and RGO/PDA/g-C3N4 free standing membrane. These membrane materials were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Atomic Force Microscope (AFM) and UV–vis diffuse reflectance spectra (DRS). The RGO/PDA/g-C3N4 composites membrane achieve to continuous and simultaneous flow-through separation of oil/water emulsion and degradation of soluble organic dye under visible-light irradiation in a short time. The results demonstrate that the flux of RGO/PDA/g-C3N4 composite membrane was strongly influenced by g-C3N4 ratio. With the g-C3N4 ratio increasing, the membrane flux was gradually improved, besides, the separation rate of oil/water emulsion and retention rate of MB (methylene blue) is about 99.5% and 99.8%, respectively. Most important of all, the composite membrane can keep steady flux and high separation and retention efficiency after 5 times of recycling under visible-light irradiation. The composite membrane investigated in this study hope to become an attractive way as promising candidate for water purification.
-
bio Inspired Method for preparation of multiwall carbon nanotubes decorated superhydrophilic poly vinylidene fluoride membrane for oil water emulsion separation
Chemical Engineering Journal, 2017Co-Authors: Xi Yang, Yi He, Guangyong Zeng, Xi Chen, Dayong Qing, Feng Li, Qi ChenAbstract:Abstract The intrinsic hydrophobicity of polyvinylidene fluoride (PVDF) membrane impedes its application in the field of water treatment. In this work, hydrophilic modification of PVDF membrane is processed by a mussel-Inspired Method. Multi-wall carbon nanotubes (MWCNTs) were functionalized by grafting 3-aminopropyltriethoxysilane (APTES) firstly, and then they were directly decorated on PVDF membrane surface by dopamine copolymerizes. Energy dispersive X-ray spectrometry (EDX) mapping results exhibited that functionalized MWCNTs were well dispersed in the membrane matrix. The novel membranes turned from a hydrophobic state to superhydrophilic state (completely wetted in air within only 1 s). Besides, these provide modified membranes could be applied to separate different kinds of oil-in-water emulsions with high permeate flux (about 900 L/m 2 h under 0.09 MPa) and ultrahigh oil rejection ratio (nearly 99%). More importantly, the as-prepared superhydrophilic PVDF membranes showed durable oil-fouling repellency, which could be easily recycled with a recovery of flux ratio up to 90%. In general, this bio-Inspired modification might provide a new Method for the preparation of superhydrophilic PVDF membrane and be very promising for application on oil/water emulsion separation.
-
a modified mussel Inspired Method to fabricate tio2 decorated superhydrophilic pvdf membrane for oil water separation
Journal of Membrane Science, 2016Co-Authors: Heng Shi, Yi He, Yang Pan, Haihui Di, Guangyong Zeng, Lei Zhang, Chunli ZhangAbstract:Binding nanoparticles on complex structured materials surface is an important issue for nanotechnology, such as obtain an superwetting surface. To fabricate a highly nanoparticles decorated polymer membrane with superior surface wettability, a facile and environmentally Method stills remains challenge. Taken advantage from the mussel-Inspired Method, we reported here that the TiO2 nanoparticles were directly anchored on the surface of poly(vinylidene fluoride) (PVDF) membrane, making the intrinsic hydrophobic polymer membrane become hydrophilic, what's more, the mussel Inspired Method was modified by introducing a silane coupling agent KH550, the ability to bind nanoparticles was retained and the as-prepared membrane turn from a common hydrophilic state to superhydrophilic state. Besides, the as-prepared superhydrophilic PVDF membrane could be applied on the separation of various kinds of surfactant stabilized oil-in-water emulsion with an ultrahigh efficiency nearly 99% and also shows durable oil resistance and antifouling performance, thus make the membrane easily to recycle. In addition, due to the universality of dopamine, this surface modification Method should also be applicable on other organic–inorganic hybrid materials.
Qi Chen - One of the best experts on this subject based on the ideXlab platform.
-
a mussel Inspired Method to fabricate reduced graphene oxide g c3n4 composites membranes for catalytic decomposition and oil in water emulsion separation
Chemical Engineering Journal, 2017Co-Authors: Heng Shi, Yang Pan, Guangyong Zeng, Qi Chen, Qiangbin Yang, Li YanAbstract:Abstract In the study, the reduced graphene oxide/graphitic carbon nitride sheet membrane (RGO/PDA/g-C3N4) was fabricated by the dopamine modification and assembling the RGO/PDA/g-C3N4 composites on the surface of commercial CA (cellulose acetate) membrane, consisting of an RGO/PDA/g-C3N4-CA composites membrane and RGO/PDA/g-C3N4 free standing membrane. These membrane materials were characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), Atomic Force Microscope (AFM) and UV–vis diffuse reflectance spectra (DRS). The RGO/PDA/g-C3N4 composites membrane achieve to continuous and simultaneous flow-through separation of oil/water emulsion and degradation of soluble organic dye under visible-light irradiation in a short time. The results demonstrate that the flux of RGO/PDA/g-C3N4 composite membrane was strongly influenced by g-C3N4 ratio. With the g-C3N4 ratio increasing, the membrane flux was gradually improved, besides, the separation rate of oil/water emulsion and retention rate of MB (methylene blue) is about 99.5% and 99.8%, respectively. Most important of all, the composite membrane can keep steady flux and high separation and retention efficiency after 5 times of recycling under visible-light irradiation. The composite membrane investigated in this study hope to become an attractive way as promising candidate for water purification.
-
bio Inspired Method for preparation of multiwall carbon nanotubes decorated superhydrophilic poly vinylidene fluoride membrane for oil water emulsion separation
Chemical Engineering Journal, 2017Co-Authors: Xi Yang, Yi He, Guangyong Zeng, Xi Chen, Dayong Qing, Feng Li, Qi ChenAbstract:Abstract The intrinsic hydrophobicity of polyvinylidene fluoride (PVDF) membrane impedes its application in the field of water treatment. In this work, hydrophilic modification of PVDF membrane is processed by a mussel-Inspired Method. Multi-wall carbon nanotubes (MWCNTs) were functionalized by grafting 3-aminopropyltriethoxysilane (APTES) firstly, and then they were directly decorated on PVDF membrane surface by dopamine copolymerizes. Energy dispersive X-ray spectrometry (EDX) mapping results exhibited that functionalized MWCNTs were well dispersed in the membrane matrix. The novel membranes turned from a hydrophobic state to superhydrophilic state (completely wetted in air within only 1 s). Besides, these provide modified membranes could be applied to separate different kinds of oil-in-water emulsions with high permeate flux (about 900 L/m 2 h under 0.09 MPa) and ultrahigh oil rejection ratio (nearly 99%). More importantly, the as-prepared superhydrophilic PVDF membranes showed durable oil-fouling repellency, which could be easily recycled with a recovery of flux ratio up to 90%. In general, this bio-Inspired modification might provide a new Method for the preparation of superhydrophilic PVDF membrane and be very promising for application on oil/water emulsion separation.
Yi Zhong - One of the best experts on this subject based on the ideXlab platform.
-
triple memory networks a brain Inspired Method for continual learning
IEEE Transactions on Neural Networks, 2021Co-Authors: Liyuan Wang, Bo Lei, Jun Zhu, Yi ZhongAbstract:Continual acquisition of novel experience without interfering with previously learned knowledge, i.e., continual learning, is critical for artificial neural networks, while limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task but then fails to conduct the old tasks well. By contrast, the biological brain can effectively address catastrophic forgetting through consolidating memories as more specific or more generalized forms to complement each other, which is achieved in the interplay of the hippocampus and neocortex, mediated by the prefrontal cortex. Inspired by such a brain strategy, we propose a novel approach named triple-memory networks (TMNs) for continual learning. TMNs model the interplay of the three brain regions as a triple-network architecture of generative adversarial networks (GANs). The input information is encoded as specific representations of data distributions in a generator, or generalized knowledge of solving tasks in a discriminator and a classifier, with implementing appropriate brain-Inspired algorithms to alleviate catastrophic forgetting in each module. Particularly, the generator replays generated data of the learned tasks to the discriminator and the classifier, both of which are implemented with a weight consolidation regularizer to complement the lost information in the generation process. TMNs achieve the state-of-the-art performance of generative memory replay on a variety of class-incremental learning benchmarks on MNIST, SVHN, CIFAR-10, and ImageNet-50.
-
triple memory networks a brain Inspired Method for continual learning
arXiv: Learning, 2020Co-Authors: Liyuan Wang, Bo Lei, Jun Zhu, Yi ZhongAbstract:Continual acquisition of novel experience without interfering previously learned knowledge, i.e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task, but then fails to conduct the old tasks well. By contrast, the brain has a powerful ability to continually learn new experience without catastrophic interference. The underlying neural mechanisms possibly attribute to the interplay of hippocampus-dependent memory system and neocortex-dependent memory system, mediated by prefrontal cortex. Specifically, the two memory systems develop specialized mechanisms to consolidate information as more specific forms and more generalized forms, respectively, and complement the two forms of information in the interplay. Inspired by such brain strategy, we propose a novel approach named triple memory networks (TMNs) for continual learning. TMNs model the interplay of hippocampus, prefrontal cortex and sensory cortex (a neocortex region) as a triple-network architecture of generative adversarial networks (GAN). The input information is encoded as specific representation of the data distributions in a generator, or generalized knowledge of solving tasks in a discriminator and a classifier, with implementing appropriate brain-Inspired algorithms to alleviate catastrophic forgetting in each module. Particularly, the generator replays generated data of the learned tasks to the discriminator and the classifier, both of which are implemented with a weight consolidation regularizer to complement the lost information in generation process. TMNs achieve new state-of-the-art performance on a variety of class-incremental learning benchmarks on MNIST, SVHN, CIFAR-10 and ImageNet-50, comparing with strong baseline Methods.
-
Triple Memory Networks: a Brain-Inspired Method for Continual Learning
2020Co-Authors: Wang Liyuan, Bo Lei, Li Qian, Su Hang, Zhu Jun, Yi ZhongAbstract:Continual acquisition of novel experience without interfering previously learned knowledge, i.e. continual learning, is critical for artificial neural networks, but limited by catastrophic forgetting. A neural network adjusts its parameters when learning a new task, but then fails to conduct the old tasks well. By contrast, the brain has a powerful ability to continually learn new experience without catastrophic interference. The underlying neural mechanisms possibly attribute to the interplay of hippocampus-dependent memory system and neocortex-dependent memory system, mediated by prefrontal cortex. Specifically, the two memory systems develop specialized mechanisms to consolidate information as more specific forms and more generalized forms, respectively, and complement the two forms of information in the interplay. Inspired by such brain strategy, we propose a novel approach named triple memory networks (TMNs) for continual learning. TMNs model the interplay of hippocampus, prefrontal cortex and sensory cortex (a neocortex region) as a triple-network architecture of generative adversarial networks (GAN). The input information is encoded as specific representation of the data distributions in a generator, or generalized knowledge of solving tasks in a discriminator and a classifier, with implementing appropriate brain-Inspired algorithms to alleviate catastrophic forgetting in each module. Particularly, the generator replays generated data of the learned tasks to the discriminator and the classifier, both of which are implemented with a weight consolidation regularizer to complement the lost information in generation process. TMNs achieve new state-of-the-art performance on a variety of class-incremental learning benchmarks on MNIST, SVHN, CIFAR-10 and ImageNet-50, comparing with strong baseline Methods.Comment: 12 pages, 3 figure
Lei Zhang - One of the best experts on this subject based on the ideXlab platform.
-
a modified mussel Inspired Method to fabricate tio2 decorated superhydrophilic pvdf membrane for oil water separation
Journal of Membrane Science, 2016Co-Authors: Heng Shi, Yi He, Yang Pan, Haihui Di, Guangyong Zeng, Lei Zhang, Chunli ZhangAbstract:Binding nanoparticles on complex structured materials surface is an important issue for nanotechnology, such as obtain an superwetting surface. To fabricate a highly nanoparticles decorated polymer membrane with superior surface wettability, a facile and environmentally Method stills remains challenge. Taken advantage from the mussel-Inspired Method, we reported here that the TiO2 nanoparticles were directly anchored on the surface of poly(vinylidene fluoride) (PVDF) membrane, making the intrinsic hydrophobic polymer membrane become hydrophilic, what's more, the mussel Inspired Method was modified by introducing a silane coupling agent KH550, the ability to bind nanoparticles was retained and the as-prepared membrane turn from a common hydrophilic state to superhydrophilic state. Besides, the as-prepared superhydrophilic PVDF membrane could be applied on the separation of various kinds of surfactant stabilized oil-in-water emulsion with an ultrahigh efficiency nearly 99% and also shows durable oil resistance and antifouling performance, thus make the membrane easily to recycle. In addition, due to the universality of dopamine, this surface modification Method should also be applicable on other organic–inorganic hybrid materials.
-
in situ controlled growth of well dispersed au nanoparticles inside the channels of sba 15 using a simple bio Inspired Method for surface enhanced raman spectroscopy
RSC Advances, 2013Co-Authors: Lei Zhang, Yongheng Zhu, Daqian Wang, Fang Sun, Shaoyi JiangAbstract:Well-dispersed Au nanoparticles inside the channels of SBA-15 mesoporous silica microplates were prepared using a simple, bio-Inspired in situ controlled reduction approach. The strong enhancement of Raman scattering makes this composite material a robust and convenient substrate for surface-enhanced Raman spectroscopy (SERS).
-
a biologically Inspired sensor wakeup control Method for wireless sensor networks
Systems Man and Cybernetics, 2010Co-Authors: Yan Liang, Lei Zhang, Jiannong Cao, Rui Wang, Quan PanAbstract:This paper presents an artificial ant colony approach to distributed sensor wakeup control (SWC) in wireless sensor networks (WSN) to accomplish the joint task of surveillance and target tracking. Each sensor node is modeled as an ant, and the problem of target detection is modeled as the food locating by ants. Once the food is found, the ant will release pheromone. The communication, invalidation, and fusion of target information are modeled as the processes of pheromone diffusion, loss, and accumulation. Since the accumulated pheromone can measure the existence of a target, it is used to determine the probability of ant-searching activity in the next round. To the best of our knowledge, this is the first biologically Inspired SWC Method in the WSN. Such a biologically Inspired Method has multiple desirable advantages. First, it is distributive and does not require a centralized control or cluster leaders. Therefore, it is free of the problems caused by leader failures and can save the communication cost for leader selection. Second, it is robust to false alarms because the pheromone is accumulated temporally and spatially and thus is more reliable for wakeup control. Third, the proposed Method does not need the knowledge of node position. Two theorems are presented to analytically determine the key parameters in the Method: the minimum and maximum pheromone. Simulations are carried out to evaluate the performance of the proposed Method in comparison with representative Methods.
