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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Barone, Matthew Ray, Jaideep Domino, Stefan Sandia Natl Labs Aerosci Dept Albuquerque NM 87185 USA Sandia Natl Labs Extreme Scale Data Sci & Analyt Dept Albuquerque NM 87185 USA Sandia Natl Labs Computat Thermal & Fluid Mech Dept Albuquerque NM 87185 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

This paper explores unsupervised learning approaches for analysis and categorization of turbulent flow data. Single point statistics from several high-fidelity turbulent flow simulation data sets are classified using a Gaussian mixture model clustering algorithm. Candidate features are proposed, which include barycentric coordinates of the Reynolds stress anisotropy tensor, as well as scalar and angular invariants of the Reynolds stress and mean strain rate tensors. A feature selection algorithm is applied to the data in a sequential fashion, flow by flow, to identify a good feature set and an optimal number of clusters for each data set. The algorithm is first applied to Direct Numerical Simulation data for plane channel flow, and produces clusters that are consistent with turbulent flow theory and empirical results that divide the channel flow into a number of regions (viscous sub-layer, log layer, etc). Clusters are then identified for flow over a wavy-walled channel, flow over a bump in a channel, and flow past a square cylinder. Some clusters are closely identified with the anisotropy state of the turbulence, as indicated by the location within the barycentric map of the Reynolds stress tensor. Other clusters can be connected to physical phenomena, such as boundary layer separation and free shear layers. Exemplar points from the clusters, or prototypes, are then identified using a prototype selection method. These exemplars summarize the dataset by a factor of 10 to 1000. The clustering and prototype selection algorithms provide a foundation for physics-based, semi-automated classification of turbulent flow states and extraction of a subset of data points that can serve as the basis for the development of explainable machine-learned turbulence models.

关键词： Turbulence Turbulent Flow Reynolds Stress Gaussian Mixture Models Boundary Layer Separation Direct Numerical Simulation selection algorithm Strain Rate Tensor Turbulence Models

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Antenna Subset selection in MU large-scale MIMO Systems

Antenna Subset Selection in MU large-scale MIMO Systems

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Ni, Yan Zhang, Wence Chen, Ming Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Jiangsu Peoples R China

ISBN: (纸本)9781479903085

Using a large number of transmit and receive antennas can significantly improve system performance at the price of higher hardware costs. For a large-scale MIMO system, an efficient approach to reduce the hardware cost is antenna selection. In this paper, we propose two fast antenna selection algorithms for wireless large-scale MIMO systems. The first algorithm is aiming to maximize the downlink sum-rate at a low computation complexity. Simulation results show that the proposed algorithm achieves nearly 2 dB gain in sum-capacity than the norm based selection (NBS) algorithm and has lower computational complexity than the optimal exhaust search scheme. The second algorithm minimizes the number of selected transmit antennas while maintaining a pre-defined user rate requirement, so as to reduce the hardware cost to the extreme. The performance of the proposed scheme is verified by simulation.

关键词： multiple input multiple output systems ANTENNA antennae transmitting antennas Computer hardware Feature extraction Variable subset selection TRANSMITTING System performance complexity classes selection algorithm

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Joint Power Allocation and Relay selection for Multi-Hop Cognitive Network with ARQ

Joint Power Allocation and Relay Selection for Multi-Hop Cog...

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IEEE 23rd International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhang, Ping Wang, Ying Feng, Zhiyong Li, Rong Wei, Zhiqing Chen, Shi Beijing Univ Posts & Telecommun Minist Educ Key Lab Universal Wireless Commun Beijing 100876 Peoples R China

ISBN: (纸本)9781467325691

In this paper, we investigate the power saving issue in cognitive radio (CR) multi-hop relay network. Due to the dynamic property of the wireless channel, the quality of service (QoS) guarantee for multi-hop transmission is quite challenging. To deal with these problems, automatic repeat-request (ARQ) protocol in an end-to-end manner is incorporated. For multi-hop transmission evaluation purpose, the end-to-end packet delivery probability is put forward as a QoS indicator in this paper. Besides, by underlay spectrum sharing, each relay is possessed of a power budget (i.e., maximum transmit power) to protect primary user from suffering intolerable interference. This paper addresses the power saving problem under each relay's power budget constraint, which means the end-to-end QoS constraints can be satisfied with the minimum total power consumption for relays along the optimal path. Motivated by this, we propose a joint Lagrange dual method based power allocation and exhaustive search based relay selection algorithm to obtain the solution. Numerical simulations are presented to validate the theoretical analysis. The results show that the proposed algorithm achieves a good performance in power saving.

关键词： Multi-Hop end-to-end simulations power splitting Automatic repeat request relay power budget multi-hop transmission end-to-end Quality of service Energy sources Spread spectrum communication selection algorithm Relay networks exhaustive search

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Output-Bit selection with X-Avoidance using Multiple Counters for Test-Response Compaction

Output-Bit Selection with X-Avoidance using Multiple Counter...

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19th IEEE European Test Symposium (ETS)

作者： Lien, Wei-Cheng Lee, Kuen-Jong Chakrabarty, Krishnendu Hsieh, Tong-Yu Natl Cheng Kung Univ Dept Elect Engn Tainan 70101 Taiwan Duke Univ Dept Elect & Comp Engn Durham NC USA Natl Sun Yat Sen Univ Dept Elect Engn Kaohsiung Taiwan

ISBN: (纸本)9781479934157

Output-bit selection is a recently proposed test-response compaction approach that can effectively deal with aliasing, unknown-value, and low-diagnosis problems. This approach has been implemented using a single counter and a multiplexer without considering unknown values. Also, such an implementation may require the application of a pattern multiple times in order to observe all selected responses. In this paper, we present a multiple-counter-based architecture with a new selection algorithm that can avoid most unknown-values yet achieve high compaction ratio. The remaining small number of unknowns can then be dealt with using some simple masking logic. Experiments on IWLS' 05 circuits show that even with 16% unknown responses, all unknown values can be handled with 88.92%similar to 93.21% response-volume reduction still achieved and only a moderate increase in test-application time.

关键词： Counters compaction BIT selection Unknown test response compaction selection algorithm Aliasing

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Simplified Depth Intra Mode selection for 3D Video Compression

Simplified Depth Intra Mode Selection for 3D Video Compressi...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Gu, Zhouye Zheng, Jianhua Ling, Nam Zhang, Philipp Santa Clara Univ Santa Clara CA 95053 USA Hisilicon Technol Shenzhen Peoples R China

ISBN: (纸本)9781479934324

A simplified depth modeling mode (DMM) selection algorithm is proposed for 3D-HEVC depth intra coding. Since DMMs are designed for coding units (CUs) with edge or sharp transition, it is not efficient for smooth CU compression. Based on the observation that most CUs in a depth frame are smooth or flat, we notice most of the DMM full-Rate-Distortion (RD) search could be skipped. Using the modes in full-RD list as the skip signal, we propose a simplified DMM selection algorithm to avoid unnecessary DMM full-RD cost calculation. Since modes in full-RD list already exist, we can derive the skip signal without any additional calculation. The test result for the proposed simplified selection algorithm shows 21.8% encoding time saving with only 0.16% bitrate increasing in coded and synthesized view for All-Intra test case.

关键词： algorithm design and analysis Bit rate Encoding Image coding Prediction algorithms Three-dimensional displays Video coding three-dimensional displays algorithm design and analysis Bit rate Prediction algorithms Video coding image coding coding digital multimeters Video compression selection algorithm

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Robust EEG Channel selection across Sessions in Brain-Computer Interface Involving Stroke Patients

Robust EEG Channel Selection across Sessions in Brain-Comput...

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International Joint Conference on Neural Networks (IJCNN)

作者： Arvaneh, Mahnaz Guan, Cuntai Ang, Kai Keng Quek, Chai Agcy Sci Technol & Res Inst Infocomm Res Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore Singapore

ISBN: (纸本)9781467314909

Brain-computer interface (BCI) technology has shown the capability of improving the quality of life for people with severe motor disabilities. To improve the portability and practicability of BCI systems, it is crucial to reduce the number of EEG channels as well as to have a good reliability. However, a relatively neglected issue in the EEG channel selection studies is the robustness of selected channels across sessions. This paper investigates whether the selected channels from first session is also useful for subsequent sessions on other days for a stroke patient. For this purpose, a new robust sparse common spatial pattern (RSCSP) algorithm is proposed for optimal EEG channel selection. Thereafter, the robustness of the proposed algorithm as well as 5 existing channel selection algorithms is investigated across 12 sessions data from 11 stroke patients who performed motor imagery based-BCI rehabilitation. The experimental results show that the proposed RSCSP channel selection algorithm significantly outperforms the other channel selection algorithms, when the 8 channels selected from the first session are evaluated on the 11 subsequent sessions. Moreover, there is no significant difference between the classification results of 8 channels selected by the proposed RSCSP algorithm from the first session and the classification results of 8 optimal channels selected from the same session as the test session.

关键词： channel selection stroke patient Brain-Computer Interfaces selection algorithm Computer interfaces

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Model selection for strongly nonlinear systems 54

Model selection for strongly nonlinear systems

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54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

作者： Bisaillon, Philippe Sandhu, Rimple Khalil, Mohammad Poirel, Dominique Sarkar, Abhijit Department of Civil and Environmental Engineering Carleton University Ottawa ON K1S5B6 Canada Department of Mechanical and Aerospace Engineering Royal Military College of Canada Kingston ON K7K7B4 Canada

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Reduce Training Error of Extreme Learning Machine by Selecting Appropriate Hidden Layer Output Matrix

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Journal of Systems Science and Systems Engineering 2021年第5期30卷 552-571页

作者： Yang Lv Bang Li Jinghu Yu Yiming Ding School of Science Wuhan University of TechnologyWuhan 430070China Center for Mathematical Sciences Wuhan University of TechnologyWuhan 430070China

Extreme learning machine(ELM)is a feedforward neural network with a single layer of hidden nodes,where the weight and the bias connecting input to hidden nodes are randomly *** output weight between hidden nodes and outputs are learned by a linear *** is interesting to ask whether the training error of ELM is significantly affected by the hidden layer output matrix H,because a positive answer will enable us obtain smaller training error from better *** single hidden layer feedforward neural network(SLFN)with one input neuron,there is significant difference between the training errors of different *** find there is a reliable strong negative rank correlation between the training errors and some singular values of the Moore-Penrose generalized inverse of *** on the rank correlation,a selection algorithm is proposed to choose robust appropriate H to achieve smaller training error among numerous *** experiments are carried out to validate the selection algorithm,including tests on real data *** results show that it achieves better performance in validity,speed and robustness.

关键词： ELM SLFNs training error Moore-Penrose generalized inverse selection algorithm

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PATTERN-RECOGNITION SYSTEM FOR AUTOMATIC IDENTIFICATION OF ACOUSTIC SOURCES

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AIAA JOURNAL 1991年第2期29卷 180-186页

作者： CABELL, RH FULLER, CR Virginia Polytechnic Institute and State University Blacksburg Virginia 24061

An intelligent recognition system was designed using pattern recognition techniques to distinguish the noise signatures of five different types of acoustic sources. Information for classification was calculated from the power spectral density and autocorrelation taken from the output of a single microphone. The system included a training step where it learned to distinguish the sources and automatically select descriptive quantities for optimal classification performance. Information learned in training was stored and used to identify recordings of the five types of sources presented during testing. Results of testing indicate that the current optimal design could correctly identify 90% of the recordings. Identification of noise corrupted signatures and identification of recordings not used in training is discussed.

关键词： Power Spectral Density Wind Turbine Helicopters selection algorithm Energy Distribution Automatic Identification System Takeoff and Landing Data Acquisition Acoustic Emission Oscilloscope

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Busy tone-based channel access control for cooperative communication

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TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES 2015年第10期26卷 1173-1188页

作者： Xie, Kun Xie, Kexin He, Shiming Zhang, Daqiang Wen, Jigang Lloret, Jaime Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Beijing Univ Posts & Telecommun State key Lab Networking & Switching Technol Beijing Peoples R China Tongji Univ Sch Software Engn Shanghai 201804 Peoples R China Chinese Acad Sci Inst Comp Technol Beijing 100080 Peoples R China Univ Politecn Valencia Integrated Management Coastal Res Inst Valencia 46730 Spain

Cooperative communication has attracted many research interests in recent years because it can take advantage of the broadcast nature of wireless communications and spatial diversity to improve the network performance. Although relay nodes in cooperative communications can help improve the performance, relay nodes may cause the serious competition on wireless channel accessing, which makes the channel access problem in cooperative communications challenging. To solve this problem, based on cooperative triple busy tone multiple access, this paper proposes a media access control scheme that concurrently combines the request to send/clear to send and busy tone with a proper tone duration timer. In the scheme, three busy tones are proposed to coordinate the nodes in the network to transmit packets, which is further utilised to solve the hidden terminal and exposed terminal problems in cooperative communications. Among the three busy tones, the cooperative busy tone is set according to the relay's signal-to-noise ratio. Based on the busy tone, this paper also proposes a bandwidth efficient relay selection algorithm, in which the source node can select the best relay in a distributed way by monitoring the busy tone. The simulation results demonstrate that the proposed scheme can largely reduce the blocking time and improve the channel utilisation in wireless cooperative networks. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： Communication Research cooperative communication relay Ganglia blocking time busy tone selection algorithm

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