An integrated predictive model with an on-line updating strategy for iron precipitation in zinc hydrometallurgy

2015 ◽  
Vol 151 ◽  
pp. 62-72 ◽  
Author(s):  
Yongfang Xie ◽  
Shiwen Xie ◽  
XiaoFang Chen ◽  
WeiHua Gui ◽  
Chunhua Yang ◽  
...  
Keyword(s):  
Predictive Model ◽  
Iron Precipitation ◽  
On Line ◽  
Zinc Hydrometallurgy

Practical Research on Predictive Fuzzy-PID Control in Reactor Temperature Control

2013 ◽  
Vol 313-314 ◽  
pp. 355-358
Author(s):  
Chang Yuan Huang ◽  
Hai Peng Pan
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Pid Control ◽  
Predictive Value ◽  
System Control ◽  
Reference Trajectory ◽  
Fuzzy Pid ◽  
Fuzzy Pid Control ◽  
Temperature Signal ◽  
On Line

Against the characteristics of the temperature in reactor such as time-delay, time-varying and difficulty to build a precise mathematical model in the chemical industry. Through the analysis of dynamic characteristics of the controlled object, the method of fuzzy-PID control was designed based on a predictive model. According to the detected temperature signal, the output deviation of the controller and the on-line identification of prediction model, this algorithm gains the predictive value which uses a generalized predictive model and the fuzzy-PID control. Then compare the predictive value with the reference trajectory to get the deviation. Finally use this deviation and the change of the deviation to optimize the PID control parameters and attain the appropriate amount of system control. The simulation results show that the fuzzy-PID control based on prediction model has strong adaptability, good robustness, control accuracy and higher practical value.

scholarly journals FOCUS ON ARTIFICIAL INTELLIGENCE FOR PREDICTING THE OUTFLOW OF CLIENTS FROM ON-LINE EDUCATION SITES

2021 ◽  
Vol 6 (166) ◽  
pp. 2-7
Author(s):  
V. Bredikhin ◽  
T. Senchuk ◽  
K. Stuzhuk
Keyword(s):  
Weibull Distribution ◽  
Predictive Model ◽  
Business Model ◽  
Likelihood Function ◽  
Machine Learning Algorithms ◽  
Behavioral Characteristics ◽  
Educational Product ◽  
Outflow Rate ◽  
Client Behavior ◽  
On Line

The article examines the process of forecasting customer outflows, which is especially important for companies that use a business model based on subscription. It was found that the outflow rate is extremely important for companies with a subscription and transactional business model, which implies regular payments to the company (banks, telecom operators, SaaS-services, etc.). For this purpose, the types, the main reasons for the outflow of customers and the parameters defined to build a predictive model using machine learning algorithms were considered. The result was the hypothesis of the reasons for the outflow of customers from sites that provide training services based on courses that are presented on-line in the Internet space. To build a model of outflow forecasting, the behavioral characteristics of students, their motivation and the structure of the courses themselves were studied. Based on the collected large array of data, their change was analyzed by a large number of parameters and the relationships between the behavioral characteristics of students, course structures and their passage were identified. A variant of the forecasting model was built, for which the accuracy of its operation was increased and the results were integrated into the customer outflow prediction module. The final list of features included more than 100 parameters, which were divided into 6 blocks. As a result, a predictive model was created using the Weibull distribution, as client behavior can be considered as a kind of survival model. To estimate the probability of customer outflow, based on the considered hypotheses, a recurrent neural network with an LSTM layer was developed, where a negative logarithmic likelihood function was used as a loss function for the Weibull distribution. As a conclusion, it was proposed to introduce a stable proactive educational business, when decisions are made not only on the basis of feelings, but also on the basis of data, comes a clearer and more sound understanding of how to improve the educational product.

The on-line use of histogram data for high-speed correction and alignment of electron microscope images

1984 ◽  
Vol 42 ◽  
pp. 556-557
Author(s):  
William Krakow
Keyword(s):  
Power Spectrum ◽  
High Speed ◽  
Direct Memory Access ◽  
Digital Television ◽  
Contrast Transfer Function ◽  
The Past ◽  
High Resolution Tem ◽  
On Line ◽  
Correction Of Astigmatism ◽  
The Fourier Transform

In the past few years on-line digital television frame store devices coupled to computers have been employed to attempt to measure the microscope parameters of defocus and astigmatism. The ultimate goal of such tasks is to fully adjust the operating parameters of the microscope and obtain an optimum image for viewing in terms of its information content. The initial approach to this problem, for high resolution TEM imaging, was to obtain the power spectrum from the Fourier transform of an image, find the contrast transfer function oscillation maxima, and subsequently correct the image. This technique requires a fast computer, a direct memory access device and even an array processor to accomplish these tasks on limited size arrays in a few seconds per image. It is not clear that the power spectrum could be used for more than defocus correction since the correction of astigmatism is a formidable problem of pattern recognition.

Image registration with a computer driven S.T.E.M.

1978 ◽  
Vol 36 (1) ◽  
pp. 98-99
Author(s):  
A.M.H. Schepman ◽  
J.A.P. van der Voort ◽  
J.E. Mellema
Keyword(s):  
Spot Size ◽  
Scanning Transmission Electron Microscope ◽  
Small Computer ◽  
Structural Studies ◽  
Area Of Interest ◽  
Transmission Electron ◽  
Scanning Transmission ◽  
Specimen Area ◽  
On Line ◽  
Minimum Distances

A Scanning Transmission Electron Microscope (STEM) was coupled to a small computer. The system (see Fig. 1) has been built using a Philips EM400, equipped with a scanning attachment and a DEC PDP11/34 computer with 34K memory. The gun (Fig. 2) consists of a continuously renewed tip of radius 0.2 to 0.4 μm of a tungsten wire heated just below its melting point by a focussed laser beam (1). On-line operation procedures were developped aiming at the reduction of the amount of radiation of the specimen area of interest, while selecting the various imaging parameters and upon registration of the information content. Whereas the theoretical limiting spot size is 0.75 nm (2), routine resolution checks showed minimum distances in the order 1.2 to 1.5 nm between corresponding intensity maxima in successive scans. This value is sufficient for structural studies of regular biological material to test the performance of STEM over high resolution CTEM.

Design and calibration of an IVEM for general 3-dimensional imaging of non-diffracting materials

1992 ◽  
Vol 50 (2) ◽  
pp. 1040-1041
Author(s):  
Neil Rowlands ◽  
Jeff Price ◽  
Michael Kersker ◽  
Seichi Suzuki ◽  
Steve Young ◽  
...  
Keyword(s):  
3D Imaging ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
3 Dimensional ◽  
3D Microstructure ◽  
Image Translation ◽  
Digital Correlation ◽  
On Line ◽  
Mechanical Stage ◽  
Projection Angle

Three-dimensional (3D) microstructure visualization on the electron microscope requires that the sample be tilted to different positions to collect a series of projections. This tilting should be performed rapidly for on-line stereo viewing and precisely for off-line tomographic reconstruction. Usually a projection series is collected using mechanical stage tilt alone. The stereo pairs must be viewed off-line and the 60 to 120 tomographic projections must be aligned with fiduciary markers or digital correlation methods. The delay in viewing stereo pairs and the alignment problems in tomographic reconstruction could be eliminated or improved by tilting the beam if such tilt could be accomplished without image translation.A microscope capable of beam tilt with simultaneous image shift to eliminate tilt-induced translation has been investigated for 3D imaging of thick (1 μm) biologic specimens. By tilting the beam above and through the specimen and bringing it back below the specimen, a brightfield image with a projection angle corresponding to the beam tilt angle can be recorded (Fig. 1a).

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