CN102680050A - Sulfur flotation liquid level measuring method based on foam image characteristic and air volume - Google Patents
Sulfur flotation liquid level measuring method based on foam image characteristic and air volume Download PDFInfo
- Publication number
- CN102680050A CN102680050A CN201210120613XA CN201210120613A CN102680050A CN 102680050 A CN102680050 A CN 102680050A CN 201210120613X A CN201210120613X A CN 201210120613XA CN 201210120613 A CN201210120613 A CN 201210120613A CN 102680050 A CN102680050 A CN 102680050A
- Authority
- CN
- China
- Prior art keywords
- liquid level
- characteristic
- value
- image
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
- B03D1/00—Flotation
- B03D1/02—Froth-flotation processes
- B03D1/028—Control and monitoring of flotation processes; computer models therefor
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention discloses a sulfur flotation liquid level measuring method based on a foam image characteristic and an air volume. The sulfur flotation liquid level measuring method comprises the following steps of: on the basis of using a foam image obtained by an industrial camera mounted in a sulfur flotation field, extracting a speed characteristic of the foam image by using a method based on macro block tracking; carrying out statistics on the pixels of a differential image so as to acquire a stability characteristic; and combining the characteristics with an air volume data collected in situ as a model to be input, and establishing a liquid level measurement model of a relevance vector machine (RVM) after removing abnormal data by using a Pauta standard. The liquid level measuring method provided by the invention has the characteristics of simplicity, convenience and rapidness, is applicable to liquid level real-time measurement in a sulfur flotation production process, effectively solves the problem that the liquid level measurement in a sulfur flotation field is inaccurate, and optimizes the production and operation of sulfur flotation, so that the liquid level measuring method provided by the invention has an important meaning for improvement of the grade of sulfur concentrate.
Description
Technical field
The invention belongs to the froth images feature extraction and the level gauging technical field of floatation process, be specially a kind of sulphur flotation level measuring method based on froth images characteristic and ventilation.
Background technology
Flotation is to use the most a kind of beneficiation method in the mineral processing, is usually directed to complex physicochemical process, its objective is to obtain high-grade concentrate product.Because the strong-hydrophobicity of sulfide; The sulphur floatation process need not to add any medicament; Only relating to physical process, mainly is to make the strong sulfide mineral of hydrophobicity from useless gangue, separate through regulating liquid level (froth bed thickness) and ventilation, obtains high-grade sulphur concentrate.
Liquid level directly has influence on the height of sulphur concentrate grade as a key operation amount of sulphur floatation process.Flotation cell is prone to turn over ore pulp when liquid level is higher, and flotation cell does not have overflow usually when liquid level is on the low side, causes concentrate grade low.Therefore, the accurate measurement of liquid level is the basis of realizing Optimization for liquid level control, researches and develops a kind of effective level measuring method and has great importance for improving the sulphur concentrate grade.
The on-the-spot float-type fluid level transmitter of sulphur flotation is solidified by ore pulp easily and is stuck in flotation cell bottom or ore pulp layer middle part; Cause that level gauging is inaccurate; Cause the operator that the mistake of liquid level is regulated; When serious even cause flotation cell emptying, do not have overflow, turn over unusual service condition such as ore pulp, have a strong impact on the sulphur concentrate grade.The height of liquid level directly influences flotation top layer foam flooding velocity and foam stabilization degree, and ventilation also can have influence on the height of liquid level, therefore; Research is based on the level measuring method of machine vision; Adopt a kind of method that liquid level is carried out non-contact measurement, for operating personnel provide level gauging value accurately, for realizing the control of sulphur floatation process Optimization for liquid level based on froth images characteristic and ventilation; Improve concentrate grade, the market competitiveness that improves enterprise has great importance.
Summary of the invention
The objective of the invention is to solve the problem of sulphur floatation process level gauging, proposed a kind of sulphur flotation level measuring method based on froth images characteristic and ventilation.Main contents of the present invention are following:
A kind of sulphur flotation level measuring method based on froth images characteristic and ventilation is characterized in that, may further comprise the steps:
The first step; Under normal production conditions; The froth images that obtains with the field erected industrial camera of sulphur flotation is the basis, adopts the method for following the tracks of based on macro block to extract the velocity characteristic of froth images, and utilizes velocity information statistical difference partial image pixel to obtain the degree of stability characteristic; Ventilation data and liquid level data in conjunction with collection in worksite make up sample set, and adopt La Yida criterion rejecting abnormalities data.
Step 1: gather the froth images that video camera obtains under the normal production conditions, extract foam speed and degree of stability characteristic;
The velocity characteristic method for distilling that employing is followed the tracks of based on macro block; The calculating principle is following: select a sub-piece (macro block) as template from a certain position of the former frame of image sequence consecutive frame; In present frame search best match position; The criterion that macro block is followed the tracks of adopts Normalized Cross Correlation Function, and its computing method are following:
Wherein
and
distinguishes representation module image and image subblock zone to be searched; The gray average of
,
difference representation module image and subgraph to be searched; The size of
,
representation template,
,
represent displacement.
Making the maximum position of cross correlation function is best match position; Utilize this position and previous frame to obtain position poor of template; And confirm time interval of two continuous frames according to frame per second, obtain the speed parameter
of this moment Pixel-level.
Utilize the foam velocity information, a back two field picture of two continuous frames image is transformed to the same position of former frame image, calculate the difference of first two field picture and changing image then, the number of pixels of difference image surpasses given threshold value and then calculates degree of stability.
Foam can be expressed as with mathematical expression:
Wherein:
,
Represent respectively the two continuous frames image (
i,
j) point grey scale pixel value.
expression froth images degree of stability threshold value.The total pixel number of
expression froth images processing region.
Step 2: according to foam speed and the degree of stability characteristic calculated; On-the-spot ventilation data and corresponding level value make up four-dimensional sample set
:
;
is the foam velocity characteristic;
is foam stabilization degree characteristic;
is on-the-spot ventilation data;
is corresponding level value, and
is number of samples;
if
=
=
(
=1;,
;
=1; 2; 3); Calculate the mean value
of each dimension data respectively; Record the standard error
of each dimension value by the Bezier formula; If certain measured value
satisfies
; Think that then corresponding
is exceptional value, the data of deletion correspondence in
.
In second step, because same liquid level can correspondingly be organized froth images and air quantity parameters more, the liquid level model comprises necessary probabilistic information, and associated vector machine (RVM) utilizes Bayesian frame to make up learning machine, and the result of output has probability density distribution.Therefore adopt the RVM method, import as model, set up the level gauging model with characteristics of image and ventilation.
Step 1: the sample set
according to the first step obtains is set up the RVM regression model;
Input, output collection for training are
(M is the number of samples after the rejecting abnormalities data), establish the model that objective function
carries noise:
Where noise
with mean zero and variance
Gaussian distribution.Wherein
;
is kernel function, and
is weight vector.The likelihood function of training sample set is:
Step 2: parameter reasoning;
The prior distribution of the weight in the definition step 1 is for depending on the Gaussian distribution of ultra parameter
, promptly
is the ultra parameter of decision weights
prior distribution in the formula, the sparse characteristic of its final decision model.According to bayesian criterion, the posteriority likelihood that can obtain weight vectors
is distributed as:
Wherein the posteriority covariance is:
Posterior Mean is:
The likelihood function formula of training sample set can be carried out integration through the weight variable, and the edge likelihood that can be depended on
and
is distributed as:
Step 3: ultra parameter optimization;
Owing to can not obtain to make the edge likelihood in the step 3 to distribute maximum
and
with analytical form, so use the estimation technique that iterates.To following formula about
differentiate; Making it is zero, can get
Wherein
;
is i Posterior Mean,
be that current
and
is according to i diagonal entry in the posteriority weight covariance matrix
that calculates gained in the step 2.
To noise
; Utilize the said method differentiate, obtain upgrading formula:
After obtaining parameter
and
, reappraise the Posterior Mean and the variance of weight.In the iteration estimation procedure; Most
value is for more and more approaching infinity; Promptly corresponding
is 0; Its corresponding basis function can be deleted, thereby reaches sparse property.Other
can stablize the convergence finite value, and corresponding with it
promptly is called associated vector.
Step 4: according to the RVM level gauging model that obtains, be the model input, measure the real-time level value with on-site real-time froth images characteristic and ventilation value.
The present invention proposes to measure sulphur flotation liquid level with froth images characteristic and ventilation; The method that employing is followed the tracks of based on macro block is extracted the velocity characteristic of froth images, and utilizes velocity information statistical difference partial image pixel to obtain the degree of stability characteristic, in conjunction with the ventilation data and the liquid level data structure sample set of collection in worksite; And employing La Yida criterion rejecting abnormalities data; And based on the RVM method, import as model with characteristics of image and ventilation, set up the level gauging model; Can avoid float type level gauge to be solidified the inaccurate problem of level gauging that causes, realize the accurate measurement of sulphur flotation liquid level by ore pulp.
The present invention has the characteristics simply and easily of calculating; Be suitable for the sulphur floatation process; Have stronger practicality, make the site operation personnel can quick and precisely measure sulphur floatation process liquid level, can effectively reduce sulphur flotation liquid level mistuning joint; Optimize the sulphur floating operation, make monthly sulphur concentrate grade improve 11.17%.
Description of drawings
Fig. 1 sulphur flotation liquid level measurement result.
Embodiment
The first step; Under normal production conditions; The froth images that obtains with the field erected industrial camera of sulphur flotation is the basis, adopts the method for following the tracks of based on macro block to extract the velocity characteristic of froth images, and utilizes velocity information statistical difference partial image pixel to obtain the degree of stability characteristic; Ventilation data and liquid level data in conjunction with collection in worksite make up sample set, and adopt La Yida criterion rejecting abnormalities data.
Step 1: gather the froth images that video camera obtains under the normal production conditions, extract foam speed and degree of stability characteristic;
The velocity characteristic method for distilling that employing is followed the tracks of based on macro block; The calculating principle is following: select a sub-piece (macro block) as template from a certain position of the former frame of image sequence consecutive frame; In present frame search best match position; The criterion that macro block is followed the tracks of adopts Normalized Cross Correlation Function, and its computing method are following:
Wherein
and
distinguishes representation module image and image subblock zone to be searched; The gray average of
,
difference representation module image and subgraph to be searched; The size of
,
representation template; According to the Feature Selection of actual sulphur flotation froth,
,
represent displacement here.
Making the maximum position of cross correlation function is best match position; Utilize this position and previous frame to obtain position poor of template; And confirm time interval of two continuous frames according to frame per second, obtain the speed parameter
of this moment Pixel-level.
Utilize the foam velocity information, a back two field picture of two continuous frames image is transformed to the same position of former frame image, calculate the difference of first two field picture and changing image then, the number of pixels of difference image surpasses given threshold value and then calculates degree of stability.
The foam stabilization degree is relevant with factors such as mineral species and aeration quantitys, the break degree of difficulty or ease of expression flotation froth.Can be expressed as with mathematical expression:
Wherein:
,
Represent respectively the two continuous frames image (
i,
j) point grey scale pixel value.
expression froth images degree of stability threshold value (here get global threshold 20% ~ 25%).The total pixel number of
expression froth images processing region is
here.
sThe difference of respective pixel gray-scale value of representing adjacent two frame froth images is less than the ratio of pixel number and total pixel number of the threshold value of regulation.This ratio is big more, and foam stability is good more.
Step 2: according to foam speed and the degree of stability characteristic calculated; On-the-spot ventilation data and corresponding level value make up four-dimensional sample set
:
;
is the foam velocity characteristic;
is foam stabilization degree characteristic;
is on-the-spot ventilation data;
is corresponding level value, and number of samples is 316;
if
=
=
(
=1;, 316;
=1; 2; 3); Calculate the mean value
of each dimension data respectively; Record the standard error
of each dimension value by the Bezier formula; If certain measured value
satisfies
; Think that then corresponding
is exceptional value, the data of deletion correspondence in
.
In second step, because same liquid level can correspondingly be organized froth images and air quantity parameters more, the liquid level model comprises necessary probabilistic information, and associated vector machine (RVM) utilizes Bayesian frame to make up learning machine, and the result of output has probability density distribution.Therefore adopt the RVM method, import as model, set up the level gauging model with characteristics of image and ventilation.
Input, output collection for training are
(300 is the number of samples after the rejecting abnormalities data), establish the model that objective function
carries noise:
Where noise
with mean zero and variance
Gaussian distribution.Wherein
;
is kernel function;
is weight vector, and the likelihood function of training sample set is:
Wherein
,
Step 2: parameter reasoning;
The prior distribution of the weight in the definition step 1 is for depending on the Gaussian distribution of ultra parameter
, promptly
is the ultra parameter of decision weights
prior distribution in the formula, the sparse characteristic of its final decision model.According to bayesian criterion, the posteriority likelihood that can obtain weight vectors
is distributed as:
Wherein the posteriority covariance is:
In the formula
.
Posterior Mean is:
The likelihood function formula of training sample set can be carried out integration through the weight variable, and the edge likelihood that can be depended on
and
is distributed as:
In the formula
.
Step 3: ultra parameter optimization;
Owing to can not obtain to make the edge likelihood in the step 3 to distribute maximum
and
with analytical form, so use the estimation technique that iterates.
vectorial initial value all is taken as 0.01 in the iterative process; Examining wide is 0.5; To following formula about
differentiate; Making it is zero, can get
Wherein
;
is i Posterior Mean,
be that current
and
is according to i diagonal entry in the posteriority weight covariance matrix
that calculates gained in the step 2.
After obtaining parameter
and
, reappraise the Posterior Mean and the variance of weight.In the iteration estimation procedure; Most
value is for more and more approaching infinity; Promptly corresponding
is 0; Its corresponding basis function can be deleted, thereby reaches sparse property.Other
can stablize the convergence finite value, and corresponding with it
promptly is called associated vector.
Step 4: according to the RVM level gauging model that obtains, be the model input, measure the real-time level value with on-site real-time froth images characteristic and ventilation value.According to on-site real-time froth images speed, degree of stability feature extraction; And collect corresponding ventilation data, the real-time measurement result of sulphur flotation liquid level that obtains is as shown in Figure 1, and (wherein, 1 is actual value; 2 is the RVM measured value), the measured value error analysis is shown in subordinate list 1.Can know that from subordinate list 1 the on-line measurement accuracy is high, error is little, can satisfy the production demand.
Sulphur flotation production scene is by a day chemical examination sulphur concentrate grade value, and the on-the-spot sulphur concentrate grade before and after this level measuring method is used is to such as shown in the subordinate list 2.Can know the level measuring method of use from subordinate list 2 based on froth images characteristic and ventilation; Can accurately survey real-time amount sulphur flotation liquid level; For important basis has been established in the accurate adjusting of sulphur floatation process liquid level, and make sulphur concentrate grade maximal value bring up to 81.78% from 72.77%.
Its result shows; Method proposed by the invention can be avoided the on-the-spot float-type fluid level transmitter of sulphur flotation to be solidified by ore pulp causing that level gauging forbidden problem; Reduced sulphur flotation liquid level mistuning joint, realized the Optimizing operation of sulphur floatation process, made monthly sulphur concentrate grade improve 11.17%.
Subordinate list 1:
Project | Maximum relative error | Average relative error | Minimum relative error |
Sulphur flotation level gauging | 18.13% | 6.46% | 0.96% |
Subordinate list 2:
Project | Monthly sulphur concentrate grade | Sulphur concentrate grade minimum value | Sulphur concentrate grade maximal value |
Before the use | 61.72% | 44.13% | 72.77% |
After the use | 72.89% | 60.45% | 81.78% |
Claims (1)
1. sulphur flotation level measuring method based on froth images characteristic and ventilation is characterized in that may further comprise the steps:
The first step; The froth images that obtains with the field erected industrial camera of sulphur flotation is the basis; The method that employing is followed the tracks of based on macro block is extracted the velocity characteristic of froth images, and utilizes velocity information statistical difference partial image pixel to obtain the degree of stability characteristic, in conjunction with the ventilation data and the liquid level data structure sample set of collection in worksite; And adopt La Yida criterion rejecting abnormalities data, be specially:
Step 1: the froth images that acquisition camera is obtained, extract foam speed and degree of stability characteristic;
The velocity characteristic method for distilling that employing is followed the tracks of based on macro block, select from a certain position of the former frame of image sequence consecutive frame piece be macro block as template, in present frame search best match position, the criterion that macro block is followed the tracks of adopts Normalized Cross Correlation Function:
Wherein
and
distinguishes representation module image and image subblock zone to be searched; The gray average of
,
difference representation module image and subgraph to be searched; The size of
,
representation template,
,
represent displacement;
Making the maximum position of cross correlation function is best match position; Utilize this position and previous frame to obtain position poor of template; And confirm time interval of two continuous frames according to frame per second, obtain the speed parameter
of this moment Pixel-level;
Utilize the foam velocity information, a back two field picture of two continuous frames image is transformed to the same position of former frame image, calculate the difference of first two field picture and changing image then, the number of pixels of difference image surpasses given threshold value and then calculates degree of stability;
The foam stabilization degree can be expressed as with mathematical expression:
Wherein:
,
Represent respectively the two continuous frames image (
i,
j) point grey scale pixel value,
Expression froth images degree of stability threshold value,
The total pixel number of expression froth images processing region;
Step 2: according to foam speed and the degree of stability characteristic calculated; On-the-spot ventilation data and corresponding level value make up four-dimensional sample set
:
;
is the foam velocity characteristic;
is foam stabilization degree characteristic;
is on-the-spot ventilation data;
is corresponding level value, and
is number of samples;
if
=
=
;
=1 wherein;
;
=1; 2; 3; Calculate the mean value
of each dimension data respectively; Record the standard error
of each dimension value by the Bezier formula; If certain measured value
satisfies
; Think that then corresponding
is exceptional value, the data of deletion correspondence in
;
In second step, because same liquid level can correspondingly be organized froth images and air quantity parameters more, the liquid level model comprises necessary probabilistic information, the associated vector machine, and promptly RVM utilizes Bayesian frame to make up learning machine, and the result of output has probability density distribution; Therefore adopt the RVM method, import as model, set up the level gauging model with characteristics of image and ventilation;
Collection is
for the input of training, output;
is the number of samples after the rejecting abnormalities data, establishes the model that objective function
carries noise:
Noise in the formula
is obeyed the Gaussian distribution that average is zero, variance is
; Wherein
;
is kernel function;
is weight vector, and the likelihood function of training sample set is:
Step 2: parameter reasoning;
The prior distribution of the weight in the definition step 1 is for depending on the Gaussian distribution of ultra parameter
, promptly
is the ultra parameter of decision weights
prior distribution in the formula; The sparse characteristic of its final decision model
According to bayesian criterion, the posteriority likelihood that can obtain weight vectors
is distributed as:
The likelihood function formula of training sample set can be carried out integration through the weight variable, and the edge likelihood that can be depended on
and
is distributed as:
Step 3: ultra parameter optimization;
Owing to can not obtain to make maximum
and
of edge likelihood distribution in the step 2 with analytical form; So use the estimation technique that iterates; To following formula about
differentiate; Making it is zero, can get
Wherein
;
is i Posterior Mean,
be that current
and
is according to i diagonal entry in the posteriority weight covariance matrix
that calculates gained in the step 2;
After obtaining parameter
and
; Reappraise the Posterior Mean and the variance of weight; In the iteration estimation procedure; Most
value is for more and more approaching infinity; Promptly corresponding
is 0; With its corresponding basis function deletion; Thereby reach sparse property; Other
can stablize the convergence finite value, and corresponding with it
promptly is called associated vector;
Step 4: the RVM level gauging model that adopts preceding 3 steps to obtain is the model input with on-site real-time froth images characteristic and ventilation value, measures the real-time level value.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201210120613.XA CN102680050B (en) | 2012-04-24 | 2012-04-24 | Sulfur flotation liquid level measuring method based on foam image characteristic and air volume |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201210120613.XA CN102680050B (en) | 2012-04-24 | 2012-04-24 | Sulfur flotation liquid level measuring method based on foam image characteristic and air volume |
Publications (2)
Publication Number | Publication Date |
---|---|
CN102680050A true CN102680050A (en) | 2012-09-19 |
CN102680050B CN102680050B (en) | 2014-03-05 |
Family
ID=46812324
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201210120613.XA Expired - Fee Related CN102680050B (en) | 2012-04-24 | 2012-04-24 | Sulfur flotation liquid level measuring method based on foam image characteristic and air volume |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN102680050B (en) |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104784978A (en) * | 2015-04-14 | 2015-07-22 | 冶金自动化研究设计院 | Liquid level defoaming device for flotation liquid level image recognition equipment |
CN107389139A (en) * | 2017-08-03 | 2017-11-24 | 尤立荣 | Micrometeor vision measurement device and vision measuring method |
CN107478287A (en) * | 2017-08-29 | 2017-12-15 | 北矿机电科技有限责任公司 | Detection method for determining optimal flotation machine inflation recovery factor beta |
CN108844955A (en) * | 2018-04-28 | 2018-11-20 | 大唐环境产业集团股份有限公司 | A kind of Desulphurization for Coal-fired Power Plant absorption tower Slurry bubble quantization device |
CN109272548A (en) * | 2018-09-28 | 2019-01-25 | 北京拓金科技有限公司 | A kind of measurement method of floatation process bubble diameter |
CN109772593A (en) * | 2019-01-25 | 2019-05-21 | 东北大学 | A kind of mineral pulp level prediction technique based on flotation froth behavioral characteristics |
CN112330588A (en) * | 2020-08-07 | 2021-02-05 | 辽宁中新自动控制集团股份有限公司 | Flotation froth image classification method |
CN113570636A (en) * | 2021-06-16 | 2021-10-29 | 北京农业信息技术研究中心 | Draught fan ventilation amount detection method and device |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101036904A (en) * | 2007-04-30 | 2007-09-19 | 中南大学 | Flotation froth image recognition device based on machine vision and the mine concentration grade forecast method |
CN101315669A (en) * | 2008-07-15 | 2008-12-03 | 北京石油化工学院 | Floatation foam image processing method and device |
CN101334844A (en) * | 2008-07-18 | 2008-12-31 | 中南大学 | Critical characteristic extraction method for flotation foam image analysis |
CN101404722A (en) * | 2008-11-13 | 2009-04-08 | 中南大学 | Floatation foam image vision monitoring apparatus |
-
2012
- 2012-04-24 CN CN201210120613.XA patent/CN102680050B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101036904A (en) * | 2007-04-30 | 2007-09-19 | 中南大学 | Flotation froth image recognition device based on machine vision and the mine concentration grade forecast method |
CN101315669A (en) * | 2008-07-15 | 2008-12-03 | 北京石油化工学院 | Floatation foam image processing method and device |
CN101334844A (en) * | 2008-07-18 | 2008-12-31 | 中南大学 | Critical characteristic extraction method for flotation foam image analysis |
CN101404722A (en) * | 2008-11-13 | 2009-04-08 | 中南大学 | Floatation foam image vision monitoring apparatus |
Non-Patent Citations (2)
Title |
---|
何桂春等: "浮选泡沫图像处理技术研究现状与进展", 《有色金属科学与工程》, vol. 2, no. 2, 30 April 2011 (2011-04-30) * |
郝元宏等: "一种新的浮选泡沫图像识别方法", 《西安交通大学学报》, vol. 45, no. 4, 30 April 2011 (2011-04-30) * |
Cited By (13)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104784978B (en) * | 2015-04-14 | 2016-08-17 | 冶金自动化研究设计院 | Flotation liquid level image recognition apparatus liquid level defoaming device |
CN104784978A (en) * | 2015-04-14 | 2015-07-22 | 冶金自动化研究设计院 | Liquid level defoaming device for flotation liquid level image recognition equipment |
CN107389139B (en) * | 2017-08-03 | 2023-01-24 | 尤立荣 | Micro-flow vision measuring device and vision measuring method |
CN107389139A (en) * | 2017-08-03 | 2017-11-24 | 尤立荣 | Micrometeor vision measurement device and vision measuring method |
CN107478287A (en) * | 2017-08-29 | 2017-12-15 | 北矿机电科技有限责任公司 | Detection method for determining optimal flotation machine inflation recovery factor beta |
CN107478287B (en) * | 2017-08-29 | 2019-10-29 | 北矿机电科技有限责任公司 | Detection method for determining optimal flotation machine inflation recovery factor beta |
CN108844955A (en) * | 2018-04-28 | 2018-11-20 | 大唐环境产业集团股份有限公司 | A kind of Desulphurization for Coal-fired Power Plant absorption tower Slurry bubble quantization device |
CN109272548A (en) * | 2018-09-28 | 2019-01-25 | 北京拓金科技有限公司 | A kind of measurement method of floatation process bubble diameter |
CN109772593A (en) * | 2019-01-25 | 2019-05-21 | 东北大学 | A kind of mineral pulp level prediction technique based on flotation froth behavioral characteristics |
CN112330588A (en) * | 2020-08-07 | 2021-02-05 | 辽宁中新自动控制集团股份有限公司 | Flotation froth image classification method |
CN112330588B (en) * | 2020-08-07 | 2023-09-12 | 辽宁中新自动控制集团股份有限公司 | Classification method for flotation foam images |
CN113570636A (en) * | 2021-06-16 | 2021-10-29 | 北京农业信息技术研究中心 | Draught fan ventilation amount detection method and device |
CN113570636B (en) * | 2021-06-16 | 2024-05-10 | 北京农业信息技术研究中心 | Method and device for detecting ventilation quantity of fan |
Also Published As
Publication number | Publication date |
---|---|
CN102680050B (en) | 2014-03-05 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN102680050B (en) | Sulfur flotation liquid level measuring method based on foam image characteristic and air volume | |
CN103544483B (en) | A kind of joint objective method for tracing based on local rarefaction representation and system thereof | |
CN104156734B (en) | A kind of complete autonomous on-line study method based on random fern grader | |
CN105869178A (en) | Method for unsupervised segmentation of complex targets from dynamic scene based on multi-scale combination feature convex optimization | |
CN104408724A (en) | Depth information method and system for monitoring liquid level and recognizing working condition of foam flotation | |
CN103324936B (en) | A kind of vehicle lower boundary detection method based on Multi-sensor Fusion | |
Liu et al. | Recognition of the operational statuses of reagent addition using dynamic bubble size distribution in copper flotation process | |
CN102646279A (en) | Anti-shielding tracking method based on moving prediction and multi-sub-block template matching combination | |
CN101615183B (en) | System and method for analyzing spatial image information and GIS based river time sequence | |
CN104463199A (en) | Rock fragment size classification method based on multiple features and segmentation recorrection | |
CN108647722B (en) | Zinc ore grade soft measurement method based on process size characteristics | |
CN104063713A (en) | Semi-autonomous on-line studying method based on random fern classifier | |
CN102693216A (en) | Method for tracking point feature based on fractional-order differentiation | |
CN115578732B (en) | Label identification method for fertilizer production line | |
CN103136534A (en) | Method and device of self-adapting regional pedestrian counting | |
CN108931621B (en) | Zinc ore grade soft measurement method based on process texture characteristics | |
CN102063727A (en) | Covariance matching-based active contour tracking method | |
CN115131561A (en) | Potassium salt flotation froth image segmentation method based on multi-scale feature extraction and fusion | |
CN114639064B (en) | Water level identification method and device | |
CN104537686A (en) | Tracing method and device based on target space and time consistency and local sparse representation | |
CN115761513A (en) | Intelligent remote sensing identification method for mountain large landslide based on semi-supervised deep learning | |
CN105405149A (en) | Composite texture feature extraction method for flotation froth image | |
CN109993772B (en) | Example level feature aggregation method based on space-time sampling | |
CN106127813B (en) | The monitor video motion segments dividing method of view-based access control model energy sensing | |
CN102136060A (en) | Method for detecting population density |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20140305 Termination date: 20150424 |
|
EXPY | Termination of patent right or utility model |