Model Complex Processing Navigation Signals when they are Detected in Terms of Interfering Reflections from the Background Fluctuation Noise

Results of an experimental study of a local navigation system based on the processing signals from broadcast sources presented. The results of the development of processing algorithms for point-to-point coordinates estimation of the object are presented. The results of the development of algorithms for trajectories estimation are presented. In performed simulation the possibility of obtaining submeter position estimation accuracy in the proposed system is shown. Development results of the navigation module demonstrator are presented. The results of experimental work in difficult navigation conditions, in the presence of shading, reflections and other factors, are presented. It is shown that the developed navigation module allows in the open space near buildings which partially obscuring the satellite systems signals to obtain accuracy higher than the GNSS navigation equipment. In indoor environment in the absence of satellite navigation signals, the developed module shows positioning accuracy not worse than 1.5 meters and provides a measurement rate 1 Hz and better.

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The Character of Low-Lying Excited States of Mixed-Ligand Metal Carbonyls. TD-DFT and CASSCF/CASPT2 Study of [W(CO)4L] (L = ethylenediamine, N,N'-dialkyl-1,4-diazabutadiene) and [W(CO)5L] (L = pyridine, 4-cyanopyridine)

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc20030089 ◽

2003 ◽

Vol 68 (1) ◽

pp. 89-104 ◽

Cited By ~ 6

Author(s):

Stanislav Záliš ◽

Antonín Vlček ◽

Chantal Daniel

Keyword(s):

Excited States ◽

Substitution Effect ◽

Electronic Transitions ◽

Ligand Field ◽

Metal Carbonyls ◽

Electron Density Redistribution ◽

Time Resolved ◽

Model Complex ◽

Td Dft ◽

Time Resolved Infrared Spectroscopy

This contribution presents the results of the TD-DFT and CASSCF/CASPT2 calculations on [W(CO)4(MeDAB)] (MeDAB = N,N'-dimethyl-1,4-diazabutadiene), [W(CO)4(en)] (en = ethylenediamine), [W(CO)5(py)] (py = pyridine) and [W(CO)5(CNpy)] (CNpy = 4-cyanopyridine) complexes. Contrary to the textbook interpretation, calculations on the model complex [W(CO)4(MeDAB)] and [W(CO)5(CNpy)] show that the lowest W→MeDAB and W→CNpy MLCT excited states are immediately followed in energy by several W→CO MLCT states, instead of ligand-field (LF) states. The lowest-lying excited states of [W(CO)4(en)] system were characterized as W(COeq)2→COax CT excitations, which involve a remarkable electron density redistribution between axial and equatorial CO ligands. [W(CO)5(py)] possesses closely-lying W→CO and W→py MLCT excited states. The calculated energies of these states are sensitive to the computational methodology used and can be easily influenced by a substitution effect. The calculated shifts of [W(CO)4(en)] stretching CO frequencies due to excitation are in agreement with picosecond time-resolved infrared spectroscopy experiments and confirm the occurrence of low-lying M→CO MLCT transitions. No LF electronic transitions were found for either of the complexes studied in the region up to 4 eV.

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Integrative Data Analysis from a Unifying Research Synthesis Perspective

10.1093/oso/9780190676001.003.0020 ◽

2018 ◽

Author(s):

Eun-Young Mun ◽

Anne E. Ray

Keyword(s):

Data Analysis ◽

Large Scale ◽

Research Synthesis ◽

Alcohol Intervention ◽

Data Set ◽

Integrative Data Analysis ◽

Level Data ◽

Model Complex ◽

Wide Range ◽

Individual Participant

Integrative data analysis (IDA) is a promising new approach in psychological research and has been well received in the field of alcohol research. This chapter provides a larger unifying research synthesis framework for IDA. Major advantages of IDA of individual participant-level data include better and more flexible ways to examine subgroups, model complex relationships, deal with methodological and clinical heterogeneity, and examine infrequently occurring behaviors. However, between-study heterogeneity in measures, designs, and samples and systematic study-level missing data are significant barriers to IDA and, more broadly, to large-scale research synthesis. Based on the authors’ experience working on the Project INTEGRATE data set, which combined individual participant-level data from 24 independent college brief alcohol intervention studies, it is also recognized that IDA investigations require a wide range of expertise and considerable resources and that some minimum standards for reporting IDA studies may be needed to improve transparency and quality of evidence.

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Semi-Supervised Classification via Hypergraph Convolutional Extreme Learning Machine

Applied Sciences ◽

10.3390/app11093867 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3867

Author(s):

Zhewei Liu ◽

Zijia Zhang ◽

Yaoming Cai ◽

Yilin Miao ◽

Zhikun Chen

Keyword(s):

Extreme Learning Machine ◽

Supervised Classification ◽

Structured Data ◽

Model Complex ◽

Euclidean Domain ◽

Noise Data ◽

Data Points ◽

Learning Machine ◽

Random Hypergraph ◽

Special Case

Extreme Learning Machine (ELM) is characterized by simplicity, generalization ability, and computational efficiency. However, previous ELMs fail to consider the inherent high-order relationship among data points, resulting in being powerless on structured data and poor robustness on noise data. This paper presents a novel semi-supervised ELM, termed Hypergraph Convolutional ELM (HGCELM), based on using hypergraph convolution to extend ELM into the non-Euclidean domain. The method inherits all the advantages from ELM, and consists of a random hypergraph convolutional layer followed by a hypergraph convolutional regression layer, enabling it to model complex intraclass variations. We show that the traditional ELM is a special case of the HGCELM model in the regular Euclidean domain. Extensive experimental results show that HGCELM remarkably outperforms eight competitive methods on 26 classification benchmarks.

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Complex Uncertainty of Surface Data Modeling via the Type-2 Fuzzy B-Spline Model

Mathematics ◽

10.3390/math9091054 ◽

2021 ◽

Vol 9 (9) ◽

pp. 1054

Author(s):

Rozaimi Zakaria ◽

Abd. Fatah Wahab ◽

Isfarita Ismail ◽

Mohammad Izat Emir Zulkifly

Keyword(s):

Complex Surface ◽

Complex Data ◽

Fuzzy Data ◽

Control Points ◽

B Spline ◽

Spline Surface ◽

Data Control ◽

Surface Data ◽

Model Complex

This paper discusses the construction of a type-2 fuzzy B-spline model to model complex uncertainty of surface data. To construct this model, the type-2 fuzzy set theory, which includes type-2 fuzzy number concepts and type-2 fuzzy relation, is used to define the complex uncertainty of surface data in type-2 fuzzy data/control points. These type-2 fuzzy data/control points are blended with the B-spline surface function to produce the proposed model, which can be visualized and analyzed further. Various processes, namely fuzzification, type-reduction and defuzzification are defined to achieve a crisp, type-2 fuzzy B-spline surface, representing uncertainty complex surface data. This paper ends with a numerical example of terrain modeling, which shows the effectiveness of handling the uncertainty complex data.

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Ranging technology using signals of opportunity of non-cooperative communication satellites

Journal of Navigation ◽

10.1017/s0373463320000600 ◽

2020 ◽

pp. 1-13

Author(s):

Yuanyuan Gao ◽

Yu Hua ◽

Yu Xiang ◽

Changjiang Huang ◽

Shanhe Wang ◽

...

Keyword(s):

Cooperative Communication ◽

Estimation Algorithm ◽

Time Difference ◽

Difference Measurement ◽

Test Platform ◽

Positioning Technique ◽

And Performance ◽

Navigation Signals ◽

To Receive ◽

Signals Of Opportunity

Abstract The positioning technique employing the ubiquitous signals of opportunity of non-cooperative satellites does not send special navigation signals, instead it passively receives satellite signals as noise, presenting advantages of concealment and difficulty for potential attackers. Thus, this study investigates the ranging principle and model using non-cooperative communication satellites and a time difference estimation algorithm. The technology of time difference measurement under non-cooperative observation mode was determined and simulated. A test platform for time difference measurement was built to receive the signal from an unknown geostationary Earth orbit communication satellite and verify the ranging feasibility and performance. The ranging accuracy was found to be smaller than 6 m, as demonstrated by experimental data, which shows the viability of the proposed positioning technique for ranging technology.

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Hospital length of stay for COVID-19 patients: Data-driven methods for forward planning

BMC Infectious Diseases ◽

10.1186/s12879-021-06371-6 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Bindu Vekaria ◽

Christopher Overton ◽

Arkadiusz Wiśniowski ◽

Shazaad Ahmad ◽

Andrea Aparicio-Castro ◽

...

Keyword(s):

Length Of Stay ◽

Hospital Admission ◽

Failure Time ◽

National Level ◽

Hospital Length ◽

Survival Model ◽

Data Driven ◽

Hospital Length Of Stay ◽

Similar Data ◽

Model Complex

Abstract Background Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 8.0 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 18.9 days. Conclusions Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases, instead correcting for truncation in the data, but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

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