In this paper,a novel modeling method of soft sensing is presented to measure the spent catalyst’s carbon content in fluidized-bed catalytic *** model focuses on improving the existing soft sensing models’ performan...
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ISBN:
(纸本)9781538629185
In this paper,a novel modeling method of soft sensing is presented to measure the spent catalyst’s carbon content in fluidized-bed catalytic *** model focuses on improving the existing soft sensing models’ performances,such as higher measurement accuracy,more robust working performance,or better generalization *** build this model,firstly,we choose the primary variables and secondary variables by analyzing the mechanism model of fluidized-bed catalytic ***,we build a 4 layers deep belief networks as a feature extraction structure to extract feature data from the input *** then we build an improved least squares support vector regression which is optimized by artificial bee colony algorithm as the regression *** extracted feature data will be the input to the regression ***,we use the catalyst related production data of 2# fluidized-bed catalytic cracking device from Sinopec Jiujiang Branch Corporation to train the *** simulation results verify the effectiveness of the presented method.
As a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection, TLD still exists some drawbacks and challenges that have to be addressed in order to get a mo...
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ISBN:
(纸本)9781467374439
As a novel tracking framework that explicitly decomposes the long-term tracking task into tracking, learning and detection, TLD still exists some drawbacks and challenges that have to be addressed in order to get a more reliable and general system, such as the manual initialization of tracking region and the bad adaptation in case of full out-of-plane rotation and strong deformation. In this paper, we put forward a framework of motion detection and recognition to solve the manual initialization problem. In addition, the components of the tracking points of original tracker have been transformed into partial ORB feature points at more reliable position, which could also develop the performance of detector and learning in turn. Experiments show that the improved TLD achieves higher precision, especially for out-of-plane rotation and strong deformation.
This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D *** current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time *...
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This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D *** current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time *** address these issues,we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space ***,an indoor RGB-D image semantic segmentation network is proposed,which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud ***,Bayesian updating is used to conduct incremental semantic label fusion on the established spatial point cloud *** also employ dense conditional random fields(CRF)to optimize the 3D semantic map model,resulting in a high-precision spatial semantic map of indoor *** results show that the proposed semantic mapping system can process image sequences collected by RGB-D sensors in real-time and output accurate semantic segmentation results of indoor scene images and the current local spatial semantic ***,it constructs a globally consistent high-precision indoor scenes 3D semantic map.
The domain of attraction of a class of fractional order systems subject to saturating actuators is investigated in this paper. We show the domain of attraction is the convex hull of a set of ellipsoids. In this paper,...
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In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational *** online algorithms is...
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In generalized Nash equilibrium(GNE)seeking problems over physical networks such as power grids,the enforcement of network constraints and time-varying environment may bring high computational *** online algorithms is recognized as a promising method to cope with this challenge,where the task of computing system states is replaced by directly using measured values from the physical *** this paper,we propose an online distributed algorithm via measurement feedback to track the GNE in a time-varying networked resource sharing *** that some system states are not measurable and measurement noise always exists,a dynamic state estimator is incorporated based on a Kalman filter,rendering a closed-loop dynamics of measurement-feedback driven online *** prove that,with a fixed step size,this online algorithm converges to a neighborhood of the GNE in *** simulations validate the theoretical results.
Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate t...
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Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane *** Brain web server is free for academic use and available at ***/bioinf/Mem Brain/.
This paper studies the leaderless consensus problems of multi-agent systems with input saturation and intermittent communication over directed networks. Both the state feedback and the output feedback consensus algori...
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This paper studies the leaderless consensus problems of multi-agent systems with input saturation and intermittent communication over directed networks. Both the state feedback and the output feedback consensus algorithms are developed based on low gain feedback approach. The convergence of the trajectories of all agents can be achieved by these proposed algorithms, if the communication topology has a directed spanning tree, and the intermittent communication period T and time rate ρ are larger than their associated threshold values. Simulation examples are provided to verify the theoretical results.
Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and...
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Lipid nanoparticles(LNPs)are nanocarriers composed of four lipid components and can be used for gene therapy,protein replacement,and vaccine ***,LNPs also face several challenges,such as toxicity,immune activation,and low delivery *** overcome these challenges,artificial intelligence can be used to optimize the design and formulation of LNPs,as well as to predict their properties and ***,antibody-targeted conjugation can be used to enhance the specificity and selectivity of LNPs by attaching an antibody that recognizes a specific antigen on the cell surface to LNPs.
The Convolutional neural networks (CNN) almost always consists of spatial pooling, which reduce the spatial resolution of feature-maps without any trainable parameters. This not only facilitates the network to go deep...
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In this paper,a two-degree-of-freedom(TDOF) control scheme for multi-input/multi-output(MIMO) systems with multiple time delays is ***,a novel inversing decoupler design method based on the ideal decoupling is propose...
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ISBN:
(纸本)9781479947249
In this paper,a two-degree-of-freedom(TDOF) control scheme for multi-input/multi-output(MIMO) systems with multiple time delays is ***,a novel inversing decoupler design method based on the ideal decoupling is proposed for decoupling the MIMO system with multiple time delays into several same independent single-input/single output(SISO) *** the H2 proportion-integral-derivative(PID) controller based on the internal model control(IMC) is applied to the obtained SISO ***,the second controller is developed according to the decoupled closed-loop transfer *** the decoupled SISO systems are the same,only one PID controller should be designed for the independent SISO *** proposed method can simplify the design task ***,because the TDOF control structure can isolate the disturbance from the reference,the controller of the reference loop can be used to improve the reference response *** are provided to illustrate the effectiveness of the proposed design approach.
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