Spectrum sharing is a promising technique responsible for providing efficient and fair spectrum allocation. Considering the unevenness phenomenon of spectrum usage in industrial wireless networks, a novel spectrum sha...
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Spectrum sharing is a promising technique responsible for providing efficient and fair spectrum allocation. Considering the unevenness phenomenon of spectrum usage in industrial wireless networks, a novel spectrum sharing scheme, in the framework of industrial cognitive radio network (ICRN), is proposed in this paper via an autonomous switching technique to equalize the spectrum usage and increase the spectrum access possibility of new requests. The autonomous switching technique borrows the idea from the fact that specific collective motion can be achieved by local actions of individuals in many biological systems. The accessed nodes sense the limited spectrum range around their central frequency and then make the decision of channel switching autonomously. Several sensing report based rules are presented to facilitate the switching decision in order to equalize the channel usage among the sensing range of each node. It is demonstrated that by using these rules the spectrum usage becomes more even, and thus the spectrum utilization and fairness are both improved. Numerical examples are given to show the effectiveness of the proposed spectrum sharing scheme.
Multimedia data is usually represented with different low-level features, and different types of multimedia data, namely multimodal data, often coexist in many data sources. It is interesting and challenging to learn ...
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This article mainly deals with the control and stability problems of networked Hammerstein with nonlinear input. A novel predictive controller design method is proposed to offset the effect of network delay and data d...
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ISBN:
(纸本)9781479947249
This article mainly deals with the control and stability problems of networked Hammerstein with nonlinear input. A novel predictive controller design method is proposed to offset the effect of network delay and data dropout. The controller gain which depends on the time delay of the feedback channel is time-variant. Since we assume that the state is not measurable, the control signal is based on the state estimated by the observer. As for the nonlinear part of the input,We assume it satisfies a sector constraint and treat it as a input inaccuracy. Theoretical results are presented for the closed-loop stability by modeling the system as time-delay Hammerstein system with nonlinear inputs. A second-order Hammerstein system is implemented to show the enhanced performance of this control method.
The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode *** model sequence set adaptation（MSA）is the key to des...
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The variable-structure multiple-model（VSMM）approach,one of the multiple-model（MM）methods,is a popular and effective approach in handling problems with mode *** model sequence set adaptation（MSA）is the key to design a better ***,MSA methods in the literature have big room to improve both theoretically and *** this end,we propose a feedback structure based entropy approach that could fnd the model sequence sets with the smallest size under certain *** fltered data are fed back in real time and can be used by the minimum entropy（ME）based VSMM algorithms,i.e.,***,the full Markov chains are used to achieve optimal ***,the myopic method together with particle flter（PF）and the challenge match algorithm are also used to achieve sub-optimal solutions,a trade-off between practicability and *** numerical results show that the proposed algorithm provides not only refned model sets but also a good robustness margin and very high accuracy.
As batches operate at different statuses across different phases, it can be advantageous to partition the whole batch process into different phases and characterize them separately by multiple local phase models. The ...
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ISBN:
(纸本)9781467355322
As batches operate at different statuses across different phases, it can be advantageous to partition the whole batch process into different phases and characterize them separately by multiple local phase models. The conventional clustering-based phase division algorithm overlooks the time sequence of batch operation which thus may mix different time segments located within a batch into one phase. Moreover, it is hard to capture the transitions between neighboring phases. In the present work, an automatic step-wise sequential phase division algorithm is developed to capture the changes of process characteristics along time direction within each batch. Its theoretical support is framed and the related statistical characteristics are analyzed. Using this algorithm, major phases are captured and the transition regions are separated from them as separate time regions. Thus, different statistical models are developed to reflect their time-varying characteristics. The online monitoring system is set up, which can realtime judge the affiliation of each new sample and check its status by adopting the proper statistical model. Comprehensive comparison is conducted between the proposed algorithm and clustering-based phase division algorithm. Its feasibility and performance are illustrated by an injection molding process which presents typical multiphase nature as well as transition characteristics.
Network coding (NC) is one of the promising techniques to improve network throughput towards the Shannon limit. NC has been proven to be able to achieve the max-flow min-cut bound for single source multicast networks....
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ISBN:
(纸本)9781479949212
Network coding (NC) is one of the promising techniques to improve network throughput towards the Shannon limit. NC has been proven to be able to achieve the max-flow min-cut bound for single source multicast networks. For multi-information-source multicast networks, however, the question of how to use NC is still to be answered. In this paper we will focus on the simple case of multi-information-source independent network encoding. The region of admissible rate set of this case will be provided. With this rate region obtained, more advanced network encoding algorithms for multi-information source multicast networks can be further developed.
In this paper we propose the rating Correlated Topic Model for rating-based collaborative filtering, which improves the performance of the state-of-the-art Latent Semantic Models in two aspects: (1), making the predic...
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In this paper we propose the rating Correlated Topic Model for rating-based collaborative filtering, which improves the performance of the state-of-the-art Latent Semantic Models in two aspects: (1), making the prediction accuracy more robust to the topic number K;(2), improving the recommendation quality for users with few existed ratings. We achieve our goals by employing the Logistic Normal distribution to capture the correlation between latent topics following the Correlated Topic Model, as well as modifying the generative process to meet the requirement of rating-based collaborative filtering. We derive a parameter estimation algorithm based on variational inference for the proposal. Experiment results on the Movielens data set demonstrate our model's advantages on both referred aspects.
DNA sequence design is a very important task for DNA selfassembly technologies, including DNA computing, complex 3D nanostructures and nano-devices design. These experimental DNA molecules must satisfy several combina...
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Graph coloring problem (GCP) is a well-known NP-hard combinatorial optimization problem in graph theory. Solution for GCP often finds its applications to various engineering fields. So it is very important to find a f...
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Human action recognition technology has been applied to intelligent security surveillance, content-based image and video retrieval and natural user interface. How to make use of the new type of data, 3D skeleton joint...
Human action recognition technology has been applied to intelligent security surveillance, content-based image and video retrieval and natural user interface. How to make use of the new type of data, 3D skeleton joint position extracted by 3D depth camera, has been a highly active research topic. A posture representation model is proposed, which is invariant to limb length, length ratio between body parts and body orientation. This model contains polar angle and azimuthal angle of each limb in the spherical coordinate system which is established by the features of body joints. Hidden Markov Model（HMM） is exploited for recognition. Skeleton sequences of different body orientation are collected as experimental data. Experimental results demonstrate the effectiveness of our approach.
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