scholarly journals A Scrambled Image Blind Evaluation Method Based on Space and Frequency Domain

2021 ◽  
Vol 2083 (3) ◽  
pp. 032055
Author(s):  
Congli Wang ◽  
Xiong Li
Keyword(s):  
Frequency Domain ◽  
Uniform Distribution ◽  
Evaluation Method ◽  
Statistical Characteristics ◽  
Distribution Model ◽  
Distribution Characteristics ◽  
Original Image ◽  
Image Scrambling ◽  
Gray Correlation ◽  
Evaluation Algorithm

Abstract Most of the image scrambling degree evaluation algorithms rely on the statistical features of the original image, which cannot achieve blind evaluation. Based on the uniform distribution model of the statistical characteristics of ideal scrambled images, a blind evaluation algorithm of image scrambling degree combining space and frequency domain is proposed in this paper. In the space domain, the uniform distribution characteristics of the gray histogram of the ideal scrambled image are used, and the uniform distribution characteristics of the Discrete Fourier Transform (DFT) spectrogram in the frequency domain are combined with the gray correlation analysis theory. The two are weighted to realize the space and frequency domain evaluation of the scrambled image performance. The experimental results indicate that the evaluation algorithm in this paper can consider the performance evaluation of both pixel value and pixel position dislocation, avoiding the disadvantage of ineffective spatial domain evaluation when only the pixel position scrambling is performed. It has very sensitive to the histogram distribution and frequency domain features of encrypted images, and has good agreement with Human Visual System (HVS). The original image is completely independent, and it enables blind evaluation objectively.

scholarly journals Prediction of Hydraulic Automatic Transmission Reliability Using Failure Data Based on Exponential Decay Oscillation Distribution Model

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Yongming Liu ◽  
Guowen Ye ◽  
Zhuanzhe Zhao
Keyword(s):  
Probability Distribution ◽  
Frequency Domain ◽  
Exponential Decay ◽  
Failure Time ◽  
Automatic Transmission ◽  
Oscillation Amplitude ◽  
Distribution Model ◽  
Distribution Characteristics ◽  
Distribution Models ◽  
Mechanical Products

Aiming at the problem that the traditional reliability models of mechanical products are used to predict the reliability of hydraulic automatic transmission and the expected result is relatively large, firstly, the empirical distribution model line is used to statistically analyze the failure distribution law of the hydraulic automatic transmission; then, the Fourier transform is used to perform frequency domain analysis on experience distribution; on this basis, comprehensively consider the characteristics of experience distribution and frequency domain characteristics of experience distribution, constructs the reliability model of exponential decay oscillation distribution and the corresponding reliability, failure efficiency and average life calculation model; meanwhile, studies the influence of attenuation coefficient, oscillation amplitude, oscillation angle frequency, and other parameters on the probability distribution characteristics. On this basis, the established probability distribution models are adopted to fit the failure time data of hydraulic automatic gearbox carried by a forklift, and the fitting results are compared with exponential distribution models, three-parameter Weibull models, and “bathtub curve” models. The comparing results show that the established exponential decayed oscillation distribution model can better describe the probability distribution characteristics of the fault-free working time of automatic transmission, and the use of this model can obtain a smaller root mean square error. Simultaneously, the research conclusions of this paper can provide meaningful guidance and reference for the analysis of the life distribution model of mechanical products with exponentially attenuated oscillation probability density change law.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

Acceleration data quality assessment for bridge structural health monitoring via statistical and deep-learning approach

2021 ◽  
Author(s):  
Huaqiang Zhong ◽  
Limin Sun ◽  
José Turmo ◽  
Ye Xia
Keyword(s):  
Structural Health Monitoring ◽  
Data Quality ◽  
Health Monitoring ◽  
Structural Damage ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Operating Conditions ◽  
Monitoring Data ◽  
Statistical Characteristics ◽  
Structural Health

<p>In recent years, the safety and comfort problems of bridges are not uncommon, and the operating conditions of in-service bridges have received widespread attention. Many large-span key bridges have installed structural health monitoring systems and collected massive amounts of data. Monitoring data is the basis of structural damage identification and performance evaluation, and it is of great significance to analyze and evaluate its quality. This paper takes the acceleration monitoring data of the main girder and arch rib of a long-span arch bridge as the research object, analyzes and summarizes the statistical characteristics of the data, summarizes 6 abnormal data conditions, and proposes a data quality evaluation method of convolutional neural network. This paper conducts frequency statistics on the acceleration vibration amplitude of the bridge in December 2018 in hours. In order to highlight the end effect of frequency statistics, the whole is amplified and used as network input for training and data quality evaluation. The results are good. It provides another new method for structural monitoring data quality evaluation and abnormal data elimination.</p>

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

scholarly journals A Query Language for Workflow Logs

2022 ◽  
Vol 13 (2) ◽  
pp. 1-28
Author(s):  
Yan Tang ◽  
Weilong Cui ◽  
Jianwen Su
Keyword(s):  
Business Process ◽  
Evaluation Method ◽  
Ad Hoc ◽  
Query Language ◽  
Cost Model ◽  
Formal Semantics ◽  
Control Flow ◽  
Query Evaluation ◽  
Evaluation Algorithm ◽  
Laws And Policies

A business process (workflow) is an assembly of tasks to accomplish a business goal. Real-world workflow models often demanded to change due to new laws and policies, changes in the environment, and so on. To understand the inner workings of a business process to facilitate changes, workflow logs have the potential to enable inspecting, monitoring, diagnosing, analyzing, and improving the design of a complex workflow. Querying workflow logs, however, is still mostly an ad hoc practice by workflow managers. In this article, we focus on the problem of querying workflow log concerning both control flow and dataflow properties. We develop a query language based on “incident patterns” to allow the user to directly query workflow logs instead of having to transform such queries into database operations. We provide the formal semantics and a query evaluation algorithm of our language. By deriving an accurate cost model, we develop an optimization mechanism to accelerate query evaluation. Our experiment results demonstrate the effectiveness of the optimization and achieves up to 50× speedup over an adaption of existing evaluation method.

scholarly journals Different distributions of gold nanoparticles on the tumor and calculation of dose enhancement factor by Monte Carlo simulation

2019 ◽  
Vol 5 (4) ◽  
pp. 361-371 ◽  
Author(s):  
Sajad Keshavarz ◽  
Dariush Sardari
Keyword(s):  
Gold Nanoparticles ◽  
Uniform Distribution ◽  
Enhancement Factor ◽  
Radiation Energy ◽  
Distribution Model ◽  
Distribution Models ◽  
Dose Enhancement ◽  
X Ray ◽  
Nanoparticle Distribution ◽  
Nanoparticle Sizes

Gold nanoparticles can be used to increase the dose of the tumor due to its high atomic number as well as being free from apparent toxicity. The aim of this study is to evaluate the effect of distribution of gold nanoparticles models, as well as changes in nanoparticle sizes and spectrum of radiation energy along with the effects of nanoparticle penetration into surrounding tissues in dose enhancement factor DEF. Three mathematical models were considered for distribution of gold nanoparticles in the tumor, such as 1-uniform, 2- non-uniform distribution with no penetration margin and 3- non-uniform distribution with penetration margin of 2.7 mm of gold nanoparticles. For this purpose, a cube-shaped water phantom of 50 cm size in each side and a cube with 1 cm side placed at depth of 2 cm below the upper surface of the cubic phantom as the tumor was defined, and then 3 models of nanoparticle distribution were modeled. MCNPX code was used to simulate 3 distribution models. DEF was evaluated for sizes of 20, 25, 30, 50, 70, 90 and 100 nm of gold nanoparticles, and 50, 95, 250 keV and 4 MeV photon energies. In uniform distribution model the maximum DEF was observed at 100 nm and 50 keV being equal to 2.90, in non-uniform distribution with no penetration margin, the maximum DEF was measured at 100 nm and 50 keV being 1.69, and in non-uniform distribution with penetration margin of 2.7 mm, the maximum DEF was measured at 100 nm and 50 keV as 1.38, and the results have been showed that the dose was increased by injecting nanoparticles into the tumor. It is concluded that the highest DEF could be achieved in low energy photons and larger sizes of nanoparticles. Non-uniform distribution of gold nanoparticles can increase the dose and also decrease the DEF in comparison with the uniform distribution. The non-uniform distribution of nanoparticles with penetration margin showed a lower DEF than the non-uniform distribution without any margin and uniform distribution. Meanwhile, utilization of the real X-ray spectrum brought about a smaller DEF in comparison to mono-energetic X-ray photons.

Diversified Evaluation Method of Physical Education Teaching Effect based on Cloud Computing

2021 ◽  
Vol 7 (5) ◽  
pp. 2566-2576
Author(s):  
Lun Feng ◽  
Jihui Guan
Keyword(s):  
Cloud Computing ◽  
Physical Education ◽  
Evaluation Method ◽  
Current Situation ◽  
Teaching Evaluation ◽  
Evaluation Index ◽  
Education Curriculum ◽  
Evaluation Standard ◽  
Evaluation Form ◽  
Evaluation Algorithm

By using the method of literature, this paper analyzes the development of physical education teaching evaluation, and focuses on the diversified trend of the development of physical education teaching evaluation. The pluralistic trend of PE teaching evaluation is mainly reflected in the following aspects: the pluralistic function of PE teaching evaluation, the pluralistic evaluation standard, the pluralistic evaluation content, the pluralistic evaluation subject, the pluralistic evaluation method and the pluralistic evaluation form. In view of the current situation of unified evaluation of school physical education curriculum, this paper puts forward the diversified evaluation method of physical education teaching effect based on cloud computing, and standardizes the evaluation index optimization evaluation algorithm, in order to better provide reference for the development of physical education teaching.

DEVELOPEMENT AN ALGORITM FOR RECOGNIZING TEXT DATA IN DIGITAL GRAPHIC IMAGES

2021 ◽  
pp. 75-78
Author(s):  
Н.Н. Беляев ◽  
О.А. Бебенина ◽  
В.Е. Бородкина
Keyword(s):  
Frequency Domain ◽  
Recognition Algorithm ◽  
Statistical Characteristics ◽  
Discrete Cosine Transformation ◽  
Text Data ◽  
Jpeg Images ◽  
Statistical Classifiers ◽  
Distribution Of Values ◽  
Data Content ◽  
Graphic Images

Предложен алгоритм распознавания, реализующий процедуры: обучения выбранных классификаторов и распознавания текстовых данных, учитывающие статистические характеристики распределения коэффициентов частотной области цифровых графических изображениях формата JPEG. The article presents an approach to development an algorithm for recognizing text data within JPEG format digital graphic images. Considered a hypothesis about influence text data content in JPEG digital graphic images on the distribution of values of the discrete cosine transformation coefficients in the frequency domain JPEG images of the format. Statistical classifiers models that provide a solution to the problem of recognition of text data in JPEG images based on analysis of its frequency domain have been determined. A recognition algorithm is proposed that implements the following procedures: training of selected classifiers and recognition of text data, taking into account the statistical characteristics of the distribution of frequency domain coefficients in JPEG format images.

scholarly journals A Blind Calibration Model for I/Q Imbalances of Wideband Zero-IF Receivers

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1868
Author(s):  
Xiaoye Peng ◽  
Zhiyu Wang ◽  
Jiongjiong Mo ◽  
Chenge Wang ◽  
Jiarui Liu ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Finite Impulse Response ◽  
Low Cost ◽  
Classification Rule ◽  
Statistical Characteristics ◽  
High Signal ◽  
Calibration Model ◽  
Frequency Dependent ◽  
Frequency Independent ◽  
Blind Calibration

Frequency-dependent I/Q imbalance and frequency-independent I/Q imbalance are the major impairments in wideband zero-IF receivers, and they both cannot be ignored. In this paper, a blind calibration model is designed for compensating these I/Q imbalances. In order to accurately estimate the imbalance parameters with low cost, a classification rule is proposed according to the frequency-domain statistical characteristics of the received signal. The calibration points in the frequency-domain are divided into two groups. Then, the amplitude imbalance and the frequency-dependent phase imbalance are derived from the group of signal points and, separately, the frequency-independent phase imbalance is calculated from the group of noise points. In the derivation of the frequency-dependent phase imbalance, a general fitting model suitable for all signal points is proposed, which does not require special calculations for either DC point or fs/2 point. Then, a finite impulse response (FIR) real-valued filter is designed to correct the impairments of received signal. The performances of the proposed calibration model are evaluated through both simulations and experiments. The simulation results show the image rejection ratio (IRR) improvement to around 35–45 dBc at high signal-to-noise ratio (SNR). Based on the mismatched data of the ADRV9009 evaluation board, the experimental results exhibit the IRR improvement of both multi-tone and wideband signals to about 30 dBc.

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