Subspace Estimation for REMIS (Retrieval from Mixed Sampling frequency): Regular and Singular VAR-Systems

2020 ◽  
Author(s):  
Philipp Gersing
Keyword(s):  
Sampling Frequency ◽  
Subspace Estimation

FORMULATION AND SOLUTION OF OPTIMIZATION PROBLEM REDUNDANCY SYSTEM COMPRISING SITUATION CENTER

2019 ◽  
pp. 44-50
Author(s):  
A.V. Alekseev
Keyword(s):  
Analytical Model ◽  
Quality Indicators ◽  
Significant Influence ◽  
Optimization Problem ◽  
Sampling Theorem ◽  
Sampling Frequency ◽  
Generalized Sampling ◽  
Optimal Value ◽  
Situation Center ◽  
Situation Centers

The analysis of the concept, properties and features of heterogeneous redundancy in modern complex ergatic systems, including those included in the situation centers (SC). On the basis of the qualimetric paradigm, the generalized analytical model of quality and optimization of quality by private, group, summary and aggregated quality indicators is justified. Practical ways of realization of the model and methods of optimization of the objects which are a part of SC and them as a whole at the expense of reduction of structural, functional and other types of redundancy under the obligatory condition of non-reduction of the required value of quality are given. On the example of the generalized sampling theorem when choosing the optimal value of the sampling frequency of the real bandpass signal, the criticality and significant influence on the redundancy of data in their further processing in the SC is shown.

A Numerically Robust and Low-complexity Method of Signal Subspace Estimation

2011 ◽  
Vol 33 (1) ◽  
pp. 90-94
Author(s):  
Xue-bin Zhuang ◽  
Ming-quan Lu ◽  
Zhen-ming Feng
Keyword(s):  
Low Complexity ◽  
Signal Subspace ◽  
Subspace Estimation

EFFECTS OF SAMPLING FREQUENCY IN SEWAGE QUALITY SURVEY ON ESTIMATION ACCURACY OF SEWAGE CONCENTRATION: A CASE STUDY OF HUE

2019 ◽  
Vol 75 (7) ◽  
pp. III_245-III_253
Author(s):  
Ryuichi WATANABE ◽  
Hidenori HARADA ◽  
Shigeo FUJII ◽  
Hidenari YASUI
Keyword(s):  
Sampling Frequency ◽  
Estimation Accuracy

Determination of trace concentrations of copper by FIA-FAAS after preconcentration on chelating sorbents

1989 ◽  
Vol 54 (7) ◽  
pp. 1785-1794 ◽  
Author(s):  
Vlastimil Kubáň ◽  
Josef Komárek ◽  
Zbyněk Zdráhal
Keyword(s):  
Sampling Frequency ◽  
Copper Determination ◽  
Flow Rates ◽  
Sample Injection ◽  
Injection Flow ◽  
Chelating Sorbents ◽  
Set Up ◽  
Trace Concentrations ◽  
Better Than

A FIA-FAAS apparatus containing a six-channel sorption equipment with five 3 x 26 mm microcolumns packed with Spheron Oxin 1 000, Ostsorb Oxin and Ostsorb DTTA was set up. Combined with sorption from 0.002M acetate buffer at pH 4.2 and desorption with 2M-HCl, copper can be determined at concentrations up to 100, 150 and 200 μg l-1, respectively. For sample and eluent flow rates of 5.0 and 4.0 ml min-1, respectively, and a sample injection time of 5 min, the limit of copper determination is LQ = 0.3 μg l-1, repeatability sr is better than 2% and recovery is R = 100 ± 2%. The enrichment factor is on the order of 102 and is a linear function of time (volume) of sample injection up to 5 min and of the sample injection flow rate up to 11 ml min-1 for Spheron Oxin 1 000 and Ostsorb DTTA. For times of sorption of 60 and 300 s, the sampling frequency is 70 and 35 samples/h, respectively. The parameters of the FIA-FAAS determination (acetylene-air flame) are comparable to or better than those achieved by ETA AAS. The method was applied to the determination of traces of copper in high-purity water.

The Energy Response of Calorimeters

2018 ◽  
Author(s):  
Richard Wigmans
Keyword(s):  
Sampling Frequency ◽  
Integration Time ◽  
Signal Integration ◽  
Energy Response ◽  
Absorption Process ◽  
Active Materials ◽  
Unit Energy ◽  
Sampling Fraction ◽  
Signal Integration Time ◽  
Calorimeter Response

This chapter deals with the signals produced by particles that are being absorbed in a calorimeter. The calorimeter response is defined as the average signal produced per unit energy deposited in this absorption process, for example in terms of picoCoulombs per GeV. Defined in this way, a linear calorimeter has a constant response. Typically, the response of the calorimeter depends on the type of particle absorbed in it. Also, most calorimeters are non-linear for hadronic shower detection. This is the essence of the so-called non-compensation problem, which has in practice major consequences for the performance of calorimeters. The origins of this problem, and its possible solutions are described. The roles of the sampling fraction, the sampling frequency, the signal integration time and the choice of the absorber and active materials are examined in detail. Important parameters, such as the e/mip and e/h values, are defined and methods to determine their value are described.

Recurrence Characteristics Analysis of Near-Surface Wind Speed Signal with Different Sampling Frequencies

2014 ◽  
Vol 599-601 ◽  
pp. 1605-1609 ◽  
Author(s):  
Ming Zeng ◽  
Zhan Xie Wu ◽  
Qing Hao Meng ◽  
Jing Hai Li ◽  
Shu Gen Ma
Keyword(s):  
Wind Speed ◽  
Surface Wind ◽  
Surface Wind Speed ◽  
Recurrence Plot ◽  
Sampling Frequency ◽  
Near Surface ◽  
Gas Leakage ◽  
Time Period ◽  
Characteristics Analysis ◽  
Quantification Analysis

The wind is the main factor to influence the propagation of gas in the atmosphere. Therefore, the wind signal obtained by anemometer will provide us valuable clues for searching gas leakage sources. In this paper, the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) are applied to analyze the influence of recurrence characteristics of the wind speed time series under the condition of the same place, the same time period and with the sampling frequency of 1hz, 2hz, 4.2hz, 5hz, 8.3hz, 12.5hz and 16.7hz respectively. Research results show that when the sampling frequency is higher than 5hz, the trends of recurrence nature of different groups are basically unchanged. However, when the sampling frequency is set below 5hz, the original trend of recurrence nature is destroyed, because the recurrence characteristic curves obtained using different sampling frequencies appear cross or overlapping phenomena. The above results indicate that the anemometer will not be able to fully capture the detailed information in wind field when its sampling frequency is lower than 5hz. The recurrence characteristics analysis of the wind speed signals provides an important basis for the optimal selection of anemometer.

scholarly journals SCOUR: a stepwise machine learning framework for predicting metabolite-dependent regulatory interactions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Justin Y. Lee ◽  
Britney Nguyen ◽  
Carlos Orosco ◽  
Mark P. Styczynski
Keyword(s):  
Machine Learning ◽  
Metabolic Networks ◽  
Sampling Frequency ◽  
Low Noise ◽  
Training Data ◽  
High Noise ◽  
Regulatory Interactions ◽  
Learning Framework ◽  
Metabolic Systems ◽  
Noise Data

Abstract Background The topology of metabolic networks is both well-studied and remarkably well-conserved across many species. The regulation of these networks, however, is much more poorly characterized, though it is known to be divergent across organisms—two characteristics that make it difficult to model metabolic networks accurately. While many computational methods have been built to unravel transcriptional regulation, there have been few approaches developed for systems-scale analysis and study of metabolic regulation. Here, we present a stepwise machine learning framework that applies established algorithms to identify regulatory interactions in metabolic systems based on metabolic data: stepwise classification of unknown regulation, or SCOUR. Results We evaluated our framework on both noiseless and noisy data, using several models of varying sizes and topologies to show that our approach is generalizable. We found that, when testing on data under the most realistic conditions (low sampling frequency and high noise), SCOUR could identify reaction fluxes controlled only by the concentration of a single metabolite (its primary substrate) with high accuracy. The positive predictive value (PPV) for identifying reactions controlled by the concentration of two metabolites ranged from 32 to 88% for noiseless data, 9.2 to 49% for either low sampling frequency/low noise or high sampling frequency/high noise data, and 6.6–27% for low sampling frequency/high noise data, with results typically sufficiently high for lab validation to be a practical endeavor. While the PPVs for reactions controlled by three metabolites were lower, they were still in most cases significantly better than random classification. Conclusions SCOUR uses a novel approach to synthetically generate the training data needed to identify regulators of reaction fluxes in a given metabolic system, enabling metabolomics and fluxomics data to be leveraged for regulatory structure inference. By identifying and triaging the most likely candidate regulatory interactions, SCOUR can drastically reduce the amount of time needed to identify and experimentally validate metabolic regulatory interactions. As high-throughput experimental methods for testing these interactions are further developed, SCOUR will provide critical impact in the development of predictive metabolic models in new organisms and pathways.

scholarly journals Low-Frequency Signal Sampling Method Implemented in a PLC Controller Dedicated to Applications in the Monitoring of Selected Electrical Devices

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 442
Author(s):  
Marcin Jaraczewski ◽  
Ryszard Mielnik ◽  
Tomasz Gębarowski ◽  
Maciej Sułowicz
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Low Frequency ◽  
Sampling Frequency ◽  
Electrical Signal ◽  
Distortion Factor ◽  
Electrical Devices ◽  
Frequency Sampling ◽  
Sampled Signal ◽  
Band Limited

High requirements for power systems, and hence for electrical devices used in industrial processes, make it necessary to ensure adequate power quality. The main parameters of the power system include the rms-values of the current, voltage, and active and reactive power consumed by the loads. In previous articles, the authors investigated the use of low-frequency sampling to measure these parameters of the power system, showing that the method can be easily implemented in simple microcontrollers and PLCs. This article discusses the methods of measuring electrical quantities by devices with low computational efficiency and low sampling frequency up to 1 kHz. It is not obvious that the signal of 50–500 Hz can be processed using the sampling frequency of fs = 47.619 Hz because it defies the Nyquist–Shannon sampling theorem. This theorem states that a reconstruction of a sampled signal is only guaranteed possible for a bandlimit fmax < fs, where fmax is the maximum frequency of a sampled signal. Therefore, theoretically, neither 50 nor 500 Hz can be identified by such a low-frequency sampling. Although, it turns out that if we have a longer period of a stable multi-harmonic signal, which is band-limited (from the bottom and top), it allows us to map this band to the lower frequencies, thus it is possible to use the lower sampling ratio and still get enough precise information of its harmonics and rms value. The use of aliasing for measurement purposes is not often used because it is considered a harmful phenomenon. In our work, it has been used for measurement purposes with good results. The main advantage of this new method is that it achieves a balance between PLC processing power (which is moderate or low) and accuracy in calculating the most important electrical signal indicators such as power, RMS value and sinusoidal-signal distortion factor (e.g., THD). It can be achieved despite an aliasing effect that causes different frequencies to become indistinguishable. The result of the research is a proposal of error reduction in the low-frequency measurement method implemented on compact PLCs. Laboratory tests carried out on a Mitsubishi FX5 compact PLC controller confirmed the correctness of the proposed method of reducing the measurement error.

scholarly journals A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 255
Author(s):  
Lei Wang ◽  
Yigang He ◽  
Lie Li
Keyword(s):  
Deep Learning ◽  
Transmission Lines ◽  
Fault Location ◽  
Short Term Memory ◽  
Engineering Application ◽  
Energy Operator ◽  
Sampling Frequency ◽  
Learning Technology ◽  
Long Distance ◽  
Hvdc Transmission

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.

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