ALBERT

All Library Books, journals and Electronic Records Telegrafenberg

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    facet.materialart.
    Unknown
    Method to Locate Contaminant Source and Estimate Emission Strength (2013)
    Hindawi
    Details
    Publication Date: 2013-12-18
    Description: People greatly concern the issue of air quality in some confined spaces, such as spacecraft, aircraft, and submarine. With the increase of residence time in such confined space, contaminant pollution has become a main factor which endangers life. It is urgent to identify a contaminant source rapidly so that a prompt remedial action can be taken. A procedure of source identification should be able to locate the position and to estimate the emission strength of the contaminant source. In this paper, an identification method was developed to realize these two aims. This method was developed based on a discrete concentration stochastic model. With this model, a sensitivity analysis algorithm was induced to locate the source position, and a Kalman filter was used to further estimate the contaminant emission strength. This method could track and predict the source strength dynamically. Meanwhile, it can predict the distribution of contaminant concentration. Simulation results have shown the virtues of the method.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 2
    facet.materialart.
    Unknown
    Probabilistic Forecasting Method of Metro Station Environment Based on Autoregressive LSTM Network (2020)
    Hindawi
    Details
    Publication Date: 2020-06-12
    Description: With the increasing number of metros, the comfort and safety of crew and passengers in metro stations have been paid great attention. The environment forecasting has become very important for decision-making. The outputs of the traditional point prediction methods are some exact values in the future. However, it might be closer to the real conditions that the predicted variables are given a probability range with a different confidence rather than exact values. This paper proposes a probabilistic forecasting method of metro station environment based on autoregressive Long Short Term Memory (LSTM) network. It has a good performance to quantify the uncertainty of environment trend in a metro station. Seven-day field tests were carried out to obtain the measured data of 7 internal environmental parameters in a metro station and 8 external environment parameters. In order to ensure the prediction performance, the random forest algorithm is used to select the input variables for the proposed probabilistic forecasting method. The selected input variables and the previous predicted values are as the input variables to build the probabilistic forecasting model. The proposed method can realize to predict the probabilistic distribution of internal environmental parameters in a metro station. This work may contribute to prevent emergency events and regulate environment control system reasonably.
    Print ISSN: 1024-123X
    Electronic ISSN: 1563-5147
    Topics: Mathematics , Technology
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 3
    facet.materialart.
    Unknown
    Mental Workload Classification Method Based on EEG Independent Component Features (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-04-27
    Description: Excessive mental workload will reduce work efficiency, but low mental workload will cause a waste of human resources. It is very significant to study the mental workload status of operators. The existing mental workload classification method is based on electroencephalogram (EEG) features, and its classification accuracy is often low because the channel signals recorded by the EEG electrodes are a group of mixed brain signals, which are similar to multi-source mixed speech signals. It is not wise to directly analyze the mixed signals in order to distinguish the feature of EEG signals. In this study, we propose a mental workload classification method based on EEG independent components (ICs) features, which borrows from the blind source separation (BSS) idea of mixed speech signals. This presented method uses independent component analysis (ICA) to obtain pure signals, i.e., ICs. The energy features of ICs are directly extracted for classifying the mental workload, since this method directly uses ICs energy features for feature extraction. Compared with the existing solution, the proposed method can obtain better classification results. The presented method might provide a way to realize a fast, accurate, and automatic mental workload classification.
    Electronic ISSN: 2076-3417
    Topics: Natural Sciences in General
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 4
    facet.materialart.
    Unknown
    A Study on Faster R-CNN-Based Subway Pedestrian Detection with ACE Enhancement (2018)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2018-11-26
    Description: At present, the problem of pedestrian detection has attracted increasing attention in the field of computer vision. The faster regions with convolutional neural network features (Faster R-CNN) are regarded as one of the most important techniques for studying this problem. However, the detection capability of the model trained by faster R-CNN is susceptible to the diversity of pedestrians’ appearance and the light intensity in specific scenarios, such as in a subway, which can lead to the decline in recognition rate and the offset of target selection for pedestrians. In this paper, we propose the modified faster R-CNN method with automatic color enhancement (ACE), which can improve sample contrast by calculating the relative light and dark relationship to correct the final pixel value. In addition, a calibration method based on sample categories reduction is presented to accurately locate the target for detection. Then, we choose the faster R-CNN target detection framework on the experimental dataset. Finally, the effectiveness of this method is verified with the actual data sample collected from the subway passenger monitoring video.
    Electronic ISSN: 1999-4893
    Topics: Computer Science
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 5
    facet.materialart.
    Unknown
    A Study on Sensitive Bands of EEG Data under Different Mental Workloads (2019)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2019-07-22
    Description: Electroencephalogram (EEG) signals contain a lot of human body performance information. With the development of the brain–computer interface (BCI) technology, many researchers have used the feature extraction and classification algorithms in various fields to study the feature extraction and classification of EEG signals. In this paper, the sensitive bands of EEG data under different mental workloads are studied. By selecting the characteristics of EEG signals, the bands with the highest sensitivity to mental loads are selected. In this paper, EEG signals are measured in different load flight experiments. First, the EEG signals are preprocessed by independent component analysis (ICA) to remove the interference of electrooculogram (EOG) signals, and then the power spectral density and energy are calculated for feature extraction. Finally, the feature importance is selected based on Gini impurity. The classification accuracy of the support vector machines (SVM) classifier is verified by comparing the characteristics of the full band with the characteristics of the β band. The results show that the characteristics of the β band are the most sensitive in EEG data under different mental workloads.
    Electronic ISSN: 1999-4893
    Topics: Computer Science
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 6
    facet.materialart.
    Unknown
    Ensemble Learning with Stochastic Configuration Network for Noisy Optical Fiber Vibration Signal Recognition (2019)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2019-07-26
    Description: Optical fiber pre-warning systems (OFPS) based on Φ-OTDR are applied to many different scenarios such as oil and gas pipeline protection. The recognition of fiber vibration signals is one of the most important parts of this system. According to the characteristics of small sample set, we choose stochastic configuration network (SCN) for recognition. However, due to the interference of environmental and mechanical noise, the recognition effect of vibration signals will be affected. In order to study the effect of noise on signal recognition performance, we recognize noisy optical fiber vibration signals, which superimposed analog white Gaussian noise, white uniform noise, Rayleigh distributed noise, and exponentially distributed noise. Meanwhile, bootstrap sampling (bagging) and AdaBoost ensemble learning methods are combined with original SCN, and Bootstrap-SCN, AdaBoost-SCN, and AdaBoost-Bootstrap-SCN are proposed and compared for noisy signals recognition. Results show that: (1) the recognition rates of two classifiers combined with AdaBoost are higher than the other two methods over the entire noise range; (2) the recognition for noisy signals of AdaBoost-Bootstrap-SCN is better than other methods in recognition of noisy signals.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 7
    facet.materialart.
    Unknown
    Experimental and Numerical Vibration Analysis of Hydraulic Pipeline System under Multiexcitations (2020)
    Hindawi
    Details
    Publication Date: 2020-03-10
    Description: Pipeline systems in aircraft are subjected to both hydraulic pump pressure fluctuations and base excitation from the engine. This can cause fatigue failures due to excessive vibrations. Therefore, it is essential to investigate the vibration behavior of the pipeline system under multiexcitations. In this paper, experiments have been conducted to describe the hydraulic pipeline systems, in which fluid pressure excitation in pipeline is driven by the throttle valve, and the base excitation is produced by the shaker driven by a vibration controller. An improved model which includes fluid motion and base excitation is proposed. A numerical MOC-FEM approach which combined the coupling method of characteristics (MOC) and finite element method (FEM) is proposed to solve the equations. The results show that the current MOC-FEM method could predict the vibration characteristics of the pipeline with sufficient accuracy. Moreover, the pipeline under multiexcitations could produce an interesting beat phenomenon, and this dangerous phenomenon is investigated for its consequences from engineering point of view.
    Print ISSN: 1070-9622
    Electronic ISSN: 1875-9203
    Topics: Mathematics
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 8
    facet.materialart.
    Unknown
    Identifiability and Identification of Trace Continuous Pollutant Source (2014)
    Hindawi
    Details
    Publication Date: 2014-01-01
    Description: Accidental pollution events often threaten people’s health and lives, and a pollutant source is very necessary so that prompt remedial actions can be taken. In this paper, a trace continuous pollutant source identification method is developed to identify a sudden continuous emission pollutant source in an enclosed space. The location probability model is set up firstly, and then the identification method is realized by searching a global optimal objective value of the location probability. In order to discuss the identifiability performance of the presented method, a conception of a synergy degree of velocity fields is presented in order to quantitatively analyze the impact of velocity field on the identification performance. Based on this conception, some simulation cases were conducted. The application conditions of this method are obtained according to the simulation studies. In order to verify the presented method, we designed an experiment and identified an unknown source appearing in the experimental space. The result showed that the method can identify a sudden trace continuous source when the studied situation satisfies the application conditions.
    Print ISSN: 2356-6140
    Electronic ISSN: 1537-744X
    Topics: Natural Sciences in General
    Published by Hindawi
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 9
    facet.materialart.
    Unknown
    Frequency Domain Panoramic Imaging Algorithm for Ground-Based ArcSAR (2020)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2020-12-08
    Description: The ground-based arc-scanning synthetic aperture radar (ArcSAR) is capable of 360° scanning of the surroundings with the antenna fixed on a rotating arm. ArcSAR has much wider field of view when compared with conventional ground-based synthetic aperture radar (GBSAR) scanning on a linear rail. It has already been used in deformation monitoring applications. This paper mainly focuses on the accurate and fast imaging algorithms for ArcSAR. The curvature track makes the image focusing challenging and, in the classical frequency domain, fast imaging algorithms that are designed for linear rail SAR cannot be readily applied. This paper proposed an efficient frequency domain imaging algorithm for ArcSAR. The proposed algorithm takes advantage of the angular shift-invariant property of the ArcSAR signal, and it deduces the accurate matched filter in the angular-frequency domain, so panoramic images in polar coordinates with wide swath can be obtained at one time without segmenting strategy. When compared with existing ArcSAR frequency domain algorithms, the proposed algorithm is more accurate and efficient, because it has neither far range nor narrow beam antenna restrictions. The proposed method is validated by both simulation and real data. The results show that our algorithm brings the quality of image close to the time domain back-projection (BP) algorithm at a processing efficiency about two orders of magnitude better, and it has better image quality than the existing frequency domain Lee’s algorithm at a comparable processing speed.
    Electronic ISSN: 1424-8220
    Topics: Chemistry and Pharmacology , Electrical Engineering, Measurement and Control Technology
    Published by Molecular Diversity Preservation International
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
  • 10
    facet.materialart.
    Unknown
    Rapid temperature prediction method for electronic equipment cabin (2018)
    Elsevier
    Details
    Publication Date: 2018-06-01
    Print ISSN: 1359-4311
    Electronic ISSN: 1873-5606
    Topics: Energy, Environment Protection, Nuclear Power Engineering , Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Published by Elsevier
    Location Call Number Expected Availability
    BibTip Others were also interested in ...
    PAPER CURRENT
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...