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88 results about "Soft sensor" patented technology
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Soft sensor or virtual sensor is a common name for software where several measurements are processed together. Commonly soft sensors are based on control theory and also receive the name of state observer. There may be dozens or even hundreds of measurements. The interaction of the signals can be used for calculating new quantities that need not be measured. Soft sensors are especially useful in data fusion, where measurements of different characteristics and dynamics are combined. It can be used for fault diagnosis as well as control applications.
Computer method and apparatus for online process identification
InactiveUS20060111858A1Small sizeReduce stepsSimulator controlElectrical measurementsClosed loopPredictive controller
A computer method and apparatus of online automated model identification of multivariable processes is disclosed. The method and apparatus carries out automatically all the four basic steps of industrial process identification: 1) identification test signal design and generation, 2) identification plant test, 3) model identification and 4) model validation. During the automated plant test, process models will be automatically generated at a given time interval, for example, every hour, or on demand; the ongoing test can be automatically adjusted to meet the process constraints and to improve the data quality. Plant test can be in open loop operation, closed-loop operations or partly open loop and partly closed-loop. In a (partial) closed-loop plant test, any type of controller can be used which include proportional-integral-derivative (PID) controllers and any industrial model predictive controller (MPC). The obtained process models can be used as the model in advanced process controllers such as model predictive control (MPC) and linear robust control; they can also be used as inferential models or soft sensors in prediction product qualities. The apparatus can be used in new MPC controller commissioning as well as in MPC controller maintenance.
Owner:ZHU YUCAI
Hybrid cascade model-based predictive control system
ActiveUS7006900B2Reduce computational overheadReduce slopeControl mechanismSampled-variable control systemsMathematical modelDynamic models
A hybrid cascade Model-Based Predictive control (MBPC) and conventional control system for thermal processing equipment of semiconductor substrates, and more in particular for vertical thermal reactors is described. In one embodiment, the conventional control system is based on a PID controller. In one embodiment, the MBPC algorithm is based on both multiple linear dynamic mathematical models and non-linear static mathematical models, which are derived from the closed-loop modeling control data by using the closed-loop identification method. In order to achieve effective dynamic linear models, the desired temperature control range is divided into several temperature sub-ranges. For each temperature sub-range, and for each heating zone, a corresponding dynamic model is identified. During temperature ramp up / down, the control system is provided with a fuzzy control logic and inference engine that switches the dynamic models automatically according to the actual temperature. When a thermocouple (TC) temperature measurement is in failure, a software soft sensor based on dynamic model computing is used to replace the real TC sampling in its place as a control system input. Consequently, when a TC failure occurs during a process, the process can be completed without the loss of the semiconductor substrate(s) being processed.
Owner:ASM INTERNATIONAL
Disturbance attenuation in a precision servomechanism by a frequency-separated acceleration soft sensor
InactiveUS6876168B1Influence of disturbance can be reducedHigh positioning accuracyTrack finding/aligningComputer controlAccelerometerFeedback control
In servomechanisms, like for example used in disk drives, disturbances, for example, friction, shock and vibration, prevent the system positioning accuracy from further improvement. These disturbances occur in a relatively low-frequency range compared to the electrical dynamics. In the present invention, an acceleration feedback control loop using a frequency-separated acceleration soft sensor (350) replaces the conventionally used current control loop in the low frequency range, where the disturbances occur, so as to attenuate the influence of the disturbances enclosed in the loop. The current feedback continues to manage the electrical dynamics in the high-frequency range. Estimating the required acceleration signal by a soft sensor (350) eliminates the need for physical accelerometers, which reduce system reliability and increase system cost. The acceleration feedback control loop constructed with the obtained acceleration signal also makes the system more robust to the parameter inaccuracies and variations within the loop. This invention can be easily implemented with either software or hardware.
Owner:NAT UNIV OF SINGAPORE
System for computer-aided measurement of quality and/or process data in a paper machine
InactiveUS20050145357A1Compensation effectEasy to runTesting/monitoring control systemsMachine wet endCardboardData set
A system for the computer-aided measurement of quality and / or process data during the production and / or conversion of a material web, in particular paper or board web, by way of correlation with raw measured data. The raw measured data is present in the form of other quality and process data during the production or conversion process. Measurements of quality parameters in the laboratory may also be incorporated in the raw measured data. The raw measured data is combined to form data sets which in each case are determined simultaneously. Specific laboratory or quality measurements are selected as target data which, by way of at least one soft-sensor algorithm running in a computer-based operation and linking unit, can be calculated from the other data serving as input data, and each data set containing measured data, which relates approximately to the same monitored volume of web and raw material, in particular paper and paper raw material.
Owner:VOITH PATENT GMBH
Method for building adaptive soft sensor
InactiveUS20110295777A1Improve accuracyReduce in quantityTesting/monitoring control systemsDigital computer detailsModel networkSoft sensor
The invention discloses a method for building adaptive soft sensor. The method comprises the following steps. The input and schedule vectors are constructed, and a novel learning algorithm that uses online subtractive clustering is used to recursively update the structure and parameters of a local model network. Three rules are proposed for updating centers and local model coefficients of existing clusters, for generating new clusters and new models as well as for merging existing clusters and their corresponding models. Once verified, the online inferential model can be created to generate the predicted value of process. Thus, it does not need much memory space to process the method and can be easily applied to any other machine.
Owner:NATIONAL TSING HUA UNIVERSITY
Direct seeding device
The invention provides a direct seeding device, which can improve the control degree of the seeding depth by arranging a hard-soft sensor (114). The invention is characterized in that a direct seedingdevice (82), which is freely arranged at the rear portion of a running car body (12) relative to the running car body by the elevating of an elevating hydraulic cylinder (46) and an elevating connecting lever device (3), has transfer pipes (93, 95) for respectively transferring the seeds to a farmland, floats (55, 56) are arranged at the lower part of the direct seeding device (10), ground-leveling tools (27a, 27b) are freely arranged relative to the elevating of the direct seeding device (82), the ground-leveling tools (27a, 27b) are arranged in front of floats (55, 56) to contact the groundfor levelling the ground, and a soft-hard sensor (114) for detecting the soft-hard degree of the farmland is arranged between the floats (55, 56) and the ground-leveling tools (27a, 27b).
Owner:ISEKI & CO LTD
Systems and methods for advanced optimization of continuous digester operation
ActiveUS20160362838A1Continuous operationPaper-making machinesAdaptive controlPredictive controllerComputer module
A system and method for optimization of a continuous digester operation of a continuous digester are presented. The system includes a tracking module for tracking of process variables in the continuous digester operation and developing non-linear empirical model for one or more quality variables. A soft sensor module is used for deploying a soft sensor based on the non-linear empirical model and for generating soft measurements corresponding to the quality variables at different locations. A constraint management module is used for generating dynamically a set of constraints that are used by a model predictive controller for computing set points for optimization of continuous digester operation.
Owner:ABB (SCHWEIZ) AG
Nonlinear parameter varying (NPV) model identification method
ActiveUS20150120630A1High nonlinear fitting accuracySimple designSimulator controlDigital computer detailsMulti inputOperating point
The invention discloses an identification method of nonlinear parameter varying models (NPV) and belongs to the industrial identification field. The invention carries out identification tests and model identification for an identified object with nonlinear parameter varying characteristics. Firstly, the multi-input single-output nonlinear parameter varying model is identified through the steps of local nonlinear model tests, local nonlinear models identification, and operating point variable transition tests; after completing the identification of all the multi-input single-output nonlinear parameter varying models with respect to all the controlled variables, the completed multi-input multi-output nonlinear parameter varying models are built. The nonlinear parameter varying models of an identified object can be obtained by the identification method of the present invention with limited input / output data without detailed mechanism knowledge of the identified object. The nonlinear parameter varying models obtained can be used in model-based control algorithm design and process simulation, as well as in product quality prediction reasoning models and soft sensors.
Owner:ZHEJIANG UNIV
Bridge damage positioning method based on standing vehicle test
ActiveCN109839440ASimplify the analysis processImprove positioning efficiencyAnalysing solids using sonic/ultrasonic/infrasonic wavesFrequency spectrumEngineering
The invention discloses a bridge damage positioning method based on a standing vehicle test. The method is characterized in that a single wireless acceleration sensor is installed on a two-axle vehicle to form a movable test device; the two-axle vehicle is gradually placed at different positions of a bridge for testing, and dynamic responses of the two-axle vehicle and a bridge system under environmental excitation are obtained; spectrum analysis is performed on the dynamic responses through Fourier transform to obtain a corresponding frequency; and system frequency variation curves when the two-axle vehicle is located at different positions before and after damages are compared to determine a damage position of the bridge. The method can be conveniently implemented, efficiency is high anda result is visual. Problems that a traditional damage positioning method requires a large number of test sensors, testing is complicated and data processing is difficult are effectively solved.
Owner:HEFEI UNIV OF TECH
Soft-sensing method of crucial biochemical quantity in penicillin fermentation process based on fuzzy neural inverse and system structure thereof
InactiveCN101587350AHigh precisionReduce mistakesPhysical realisationTotal factory controlDifferentiatorEngineering
Soft-sensing method of crucial biochemical quantity in penicillin fermentation process based on fuzzy neural inverse and system structure thereof is a method for resloving the problem that the crucial biochemical quantity in penicillin fermentation process is difficult to be measured by physical sensor on-line and real-time. Fuzzy neural inverse soft-sensing method establishes a soft-sensor (11) model based on a kinetic equation in penicillin fermentation process (1), on this basis eatablishes an inverse model of the soft-sensor according to inverse system method, and then uses static fuzzy neural network (41) and a differentor to establish fuzzy neural inverse (4) through a free parameters determined by training the static fuzzy neural network, then the soft-sensor inverse is implemented, finally links the fuzzy neural inverse after the penicillin fermentation process to implement on-line and real-time soft-sensing of fungi concentration x[1], substrate concentration x[2] and products concentration x[3]. Specific implementation of the fuzzy neural inverse is the constructed fuzzy neural inverse system applies embedded microprocessor ARM processor.
Owner:JIANGSU UNIV
A PM2.5 measurement method based on image features and integrated neural network
The invention relates to a soft sensing method for PM2.5 of air fine particles based on image features and an integrated neural network, which belongs to the field of both environmental engineering and detection technology. An atmospheric environmental system has many variables, nonlinear and complicated internal mechanism. Compared with single neural network, an ensemble neural network has betterability to deal with highly nonlinear and seriously uncertain system, and the real-time and high efficiency of PM2.5 prediction can be improved effectively by using image features as input variables.The invention aims at the problem that PM2.5 is difficult to predict with high precision and real-time. Firstly, the image features related to PM2.5 are extracted based on the feature extraction method. Secondly, the soft sensor model between PM2.5 and the image features is established by using the ensemble neural network based on the simple average method. Finally, the PM2.5 is predicted with the established soft sensor model and good results are obtained. The output results of the soft sensor model can provide timely and accurate information of atmospheric environment quality for environmental management decision-makers and the masses, which is conducive to strengthening the control of atmospheric pollution and preventing serious pollution.
Owner:BEIJING UNIV OF TECH
System and method for wastewater treatment process control
A system for wastewater treatment process control comprising a set of measuring means arranged to obtain a dataset, the dataset comprises a plurality of process variables related to a parameter of thewastewater treatment process; a prediction module arranged to receive the dataset and predict the parameter of wastewater treatment process based on a soft sensor; a troubleshooting module arranged to compare the predicted parameter with a predetermined criterion; wherein if the predicted parameter does not satisfy the predetermined criterion; the troubleshooting module is operable to identify atleast one process variable from the plurality of process variables which causes the predicted parameter not to satisfy the predetermined criterion and determine whether the identified at least one process variable from the plurality of process variables is controllable. An optimisation module for use in a wastewater treatment system is also disclosed.
Owner:胜科水处理科技有限公司
Soft mechanical arm based on SMA springs
InactiveCN109176589ASimple drive structureNo pollution in the processGripping headsMicrocontrollerMicrocomputer
The invention discloses a soft mechanical arm based on SMA springs. The soft mechanical arm comprises a platform. Multiple mechanical arm fingers are arranged below the platform. Each mechanical arm finger comprises a silica gel outer shell. Each silica gel outer shell is internally provided with a soft sensor and the corresponding two SMA springs. Every two SMA springs are arranged in the corresponding silica gel outer shell at the set angle from top to bottom. The top end of each SMA spring is provided with a lead wire. The SMA springs are connected with a control circuits through the lead wires. The extending-and-contracting state of the SMA springs is controlled through the control circuit, and thus, work of the mechanical arm fingers is achieved. The control circuit comprises a singlechip microcomputer, a PWM electronic switch control panel and a voltage-controlled current source, wherein the single chip microcomputer is provided with multiple analog input ports. The STM32 singlechip microcomputer is used for achieving a PWM control constant flow source circuit and outputting the specific frequency and PWM waveforms of specific duty ratios. The PWM electronic switch controlpanel is used for converting PWM signals into the voltage. The voltage-controlled current source controls the current to change through voltage changes. By means of the soft mechanical arm, gripping control of the soft mechanical arm can be achieved.
Owner:GUANGZHOU UNIVERSITY
Non-Gaussian process monitoring method based on distributed ICR model
ActiveCN108445867AEasy fault detectionProgramme controlElectric testing/monitoringData modelingIndependent component analysis
The invention discloses a non-Gaussian process monitoring method based on distributed ICR (Independent Component Regression) models, aiming at solving the problem of how to use a non-Gaussian data modeling algorithm to convert sampling data into errors through a data model and the problem of implementing non-Gaussian process monitoring by taking errors as monitored objects. Specifically, the method of the invention includes the following steps: firstly, establishing a soft sensing model between each variable and other variables using an independent component regression (ICR) algorithm for eachmeasured variable; and secondly, establishing a process monitoring model based on independent component analysis (ICA) to implement non-Gaussian process monitoring using estimation errors of a soft sensor model as monitored objects. It can be seen that the method of the invention utilizes the advantages of distributed modeling and adopts an implementation mode combined with a plurality of non-Gaussian data analysis algorithms, so the method is a more preferred data-driven process monitoring method suitable for a non-Gaussian process.
Owner:NINGBO UNIV
Soft sensor device and device for evaluating the same
A case base generator divides an input space of history data into unit input spaces according to a desired output allowable error, and creates a representative case from the history data arranged in the unit input spaces, thereby generating a case base. A case retrieving section of a soft sensor retrieves a case corresponding to new input data from the case base. An output estimating section calculates and outputs estimated output data corresponding to the new input data on the basis of the output data on the retrieved case. An output evaluating section calculates and outputs an estimation error in the estimated output data on the basis of the topological distance between the new input data and the retrieved case. A function evaluating section evaluates the soft sensor on the basis of the estimation error, the estimated output data, and true output data.
Owner:YAMATAKE HONEYWELL CO LTD
System for computer-aided measurement of quality and/or process data in a paper machine
InactiveUS7437208B2Easy to runSlow changeTesting/monitoring control systemsMachine wet endCardboardData set
Owner:VOITH PATENT GMBH
Neural network inverse-based soft sensing method for compensation capacity and medium loss of capacitor and on-line monitoring
InactiveCN102331528AOvercoming strong dependenciesEasy to implementResistance/reactance/impedenceSoft sensingCircuit breaker
The invention relates to a neural network inverse-based soft sensing and soft sensor construction method for variables during an operation process of a power distribution network intelligent capacitor; that is, the invention relates to an on-line estimation method for variables, so that a problem that it is difficult to carry out on-line and real-time measurement on variables by a sensor during a capacitor operation process can be solved. According to an operation process model of a capacitor, a model of an embedded sensor is established; a neural network inverse that is in series connection after the capacitor operation process is constructed to realize a contained sensor inverse; and at last, on-line soft sensing on a compensation capacity and a medium loss of the capacitor is realized. Besides, the neural network inverse is realized by employing a digital signal processor. In addition, the invention relates to an on-line monitoring system for a power distribution network intelligent capacitor. The on-line monitoring system comprises an on-site preset device and a signal acquisition terminal. A sensor detects a corresponded physical quantity on a capacitor unit; after digital analog conversion, the detected physical quantity is sent to a digital signal processor to make fault determination; and an output of a programming logic device controls on-off of a breaker. The on-line monitoring system is sensitive and reliable; and moreover, safe operation of an intelligent capacitor can be guaranteed.
Owner:JIANGSU ZHENAN ELECTRIC POWER EQUIP
System and method for wastewater treatment process control
InactiveUS20180327292A1Minimization of overall operating expenseMinimise concentrationWater treatment parameter controlComputer controlTechnical standardWastewater
A system for wastewater treatment process control comprising a set of measuring means arranged to obtain a dataset, the dataset comprises a plurality of process variables related to a parameter of the wastewater treatment process; a prediction module arranged to receive the dataset and predict the parameter of wastewater treatment process based on a soft sensor; a troubleshooting module arranged to compare the predicted parameter with a predetermined criterion; wherein if the predicted parameter does not satisfy the predetermined criterion; the troubleshooting module is operable to identify at least one process variable from the plurality of process variables which causes the predicted parameter not to satisfy the predetermined criterion and determine whether the identified at least one process variable from the plurality of process variables is controllable. An optimisation module for use in a wastewater treatment system is also disclosed.
Owner:SEMBCORP WATERTECH PTE LTD
Public opinion analysis method based on probability feature association
InactiveCN107239562ASemantic expressionImprove fusion effectSpecial data processing applicationsFeature setOpinion analysis
Owner:EAST CHINA UNIV OF SCI & TECH
Using soft-sensors in a programmable logic controller
A method of operating an intelligent programmable logic controller over a plurality of scan cycles includes the intelligent programmable logic controller selecting one or more softsensors available in a control program corresponding to a production unit, each soft-sensor comprising a local parameter or variable used by the control program. The intelligent programmable logic controller determines updated soft-sensor values corresponding to the one or more soft-sensors during each scan cycle and stores those values during each scan cycle on a non-volatile computer-readable storage medium operably coupled to the intelligent programmable logic controller. Additionally, the intelligent programmable logic controller annotates the updated soft-sensor values with automation system context information to generate contextualized data.
Owner:SIEMENS AG
Method and apparatus for soft-sensor characterization of batteries
InactiveUS7711538B2Accurate predictionCircuit-breaking switches for excess currentsBatteries circuit arrangementsSoft sensorReliability engineering
A method and apparatus for simulating the operation of a rechargeable battery. A value is obtained for at least one parameter that describes an internal state of the battery. The obtained value is used for calculating a prediction value for a characteristic of the battery that is observable outside the battery. These steps are repeated a multitude of times in order to simulate the operation of the battery over a certain period of time. A difference is detected between a calculated prediction value and a known value of a corresponding characteristic in a corresponding situation. The obtained value of the at least one parameter is corrected before a further prediction value is calculated by an amount that is proportional to the detected difference.
Owner:INTELLECTUAL VENTURES FUND 83 LLC
Method for building adaptive soft sensor
InactiveUS8429100B2Improve accuracyReduce in quantityTesting/monitoring control systemsDigital computer detailsModel networkSoft sensor
The invention discloses a method for building adaptive soft sensor. The method comprises the following steps. The input and schedule vectors are constructed, and a novel learning algorithm that uses online subtractive clustering is used to recursively update the structure and parameters of a local model network. Three rules are proposed for updating centers and local model coefficients of existing clusters, for generating new clusters and new models as well as for merging existing clusters and their corresponding models. Once verified, the online inferential model can be created to generate the predicted value of process. Thus, it does not need much memory space to process the method and can be easily applied to any other machine.
Owner:NATIONAL TSING HUA UNIVERSITY
Auxiliary variable simplification method for high-dimensional nonlinear soft sensor model
InactiveCN103186696ASoft measurement is goodSave manpower, material and financial resourcesSpecial data processing applicationsEngineeringOptimal modeling
The invention discloses an auxiliary variable simplification method for a high-dimensional nonlinear soft sensor model, which is characterized by comprising the following steps: I, determining n primary auxiliary variables possibly related to a dominant variable, and acquiring the value data of the n primary auxiliary variables and the dominant variable to form a sample set; II, combining a KICA method and an FNN method and respectively calculating weight values of the n primary auxiliary variables; III, forming a primary auxiliary variable sequence; IV, modeling and determining the optimal auxiliary variable according to the minimum mean square error (MSE); and V, obtaining a simplification model of a soft sensor. On the basis of the optimal modeling effect, the auxiliary variable simplification method can find out an auxiliary variable set containing the least auxiliary variables to carry out modeling on the dominant variable, so as to simplify the auxiliary variables.
Owner:重庆缇帅科技有限公司
Method for screening sensitive variables influencing product quality in complex industrial process based on step-by-step reduction
ActiveCN110490496ASolve the problem of large redundancyReduce redundancyResourcesManufacturing computing systemsComputer scienceSoft sensor
The invention discloses a method for screening sensitive variables influencing product quality in a complex industrial process based on step-by-step reduction, which belongs to the technical field ofsoft measurement and comprises the following steps of: selecting auxiliary variables influencing the product quality through expert knowledge and collecting data samples; calculating an auxiliary variable sensitivity index by comprehensively considering the variable correlation and the sensitivity of the variable to the working condition change, and preliminarily screening the sensitive variable influencing the dominant variable; and constructing a weighted cosine Mahalanobis system, and accurately screening key sensitive variables influencing the product quality. According to the method, thecorrelation and the working condition information of the variables can be accurately reflected, meanwhile, the redundancy of the variables is well reduced, the product quality prediction precision canbe improved, the complexity of the prediction model can be effectively reduced, and the method also has important significance for maintenance of the soft sensor model.
Owner:CENT SOUTH UNIV
Multi-motion-pattern soft crawling robot
The invention discloses a multi-motion-pattern soft crawling robot. The multi-motion-pattern soft crawling robot comprises a first soft driver, a second soft driver and a third soft driver which are sequentially connected, a soft sensor located at the front end of the first soft driver, front feet located below the first soft driver, and rear feet located below the third soft driver, wherein eachsoft driver comprises a multi-air-bag structure and a bottom structure located at the bottom of the multi-air-bag structure. In combination with trapezoidal-cavity multi-air-bag type drivers and a soft tactile sensor, the multi-motion-pattern soft crawling robot is provided, multiple motion patterns such as crawling, crossing and climbing can be self-adaptively finished in a complex environment, and the robot has good environmental adaptability.
Owner:苏州柔性智能科技有限公司
System and method for predicting parameter of wastewater treatment process
The invention describes a method for predicting a parameter of wastewater treatment process. The method comprises the following steps: obtaining a data set comprising a plurality of process variables related to the parameter of the wastewater treatment process; obtaining a predetermined number of measurement values of the parameter to be predicted; preprocessing the obtained data set, wherein preprocessing steps comprises classifying the data set into input groups which comprise the data set and the measurement values of the parameters to be predicted; obtaining a synthesized output at each soft sensor; averaging the synthesized outputs of each soft sensor crossing the soft sensors; and setting the synthesized output after the averaging as a final prediction parameter.
Owner:胜科水处理科技有限公司
A method for on-line soft measurement of coal-fired carbon oxidation factor of pow generation boiler
ActiveCN109376501ARealization of online soft sensorReal-time monitoring and control of combustion conditionsCharacter and pattern recognitionDesign optimisation/simulationCombustionClimate change
A method for on-line soft measurement of coal-fired carbon oxidation factor of power generation boiler include such steps as selecting key variables, coal property parameter and boiler / unit property parameters as input vectors of soft measurement model, selecting them as input vectors, selecting them as input vectors, and selecting them as input vectors of soft measurement model. At the same time,the parameters of ash and fly ash after coal combustion are measured, and the carbon oxidation factor is calculated as the output vector of the soft sensor model. An on-line soft-sensing model of carbon oxidation factor was established, After establishing the soft-sensing model of coal-fired carbon oxidation factor of power generation boiler, if on-line measurement of coal-fired carbon oxidationfactor under a certain operating condition is required, only the measurement data of the following variables are assigned to the input vector X, and the calculated y is the carbon oxidation factor OFdifferent kinds of coal under different boiler loads. The invention modifies the carbon oxidation factor issued by the Intergovernmental Panel on Climate Change, so that the result is closer to the actual value. It can realize on-line measurement, real-time monitoring and boiler carbon combustion control of coal-fired boilers.
Owner:ZHEJIANG UNIV OF TECH
A Dynamic Model Identification Method for Nonlinear Processes
ActiveCN101587328BOvercome limitationsSatisfied with the control effectAdaptive controlProcess dynamicsDynamic models
The invention relates to a nonlinear process dynamic model identification method, which includes using an experiment module and an identification module, the experiment module is connected with the nonlinear industrial process through DCS or PLC or other control machines, and the experiment module and the identification module are connected to each other. The experiment module generates experiment signals and performs automatic experiments; the identification module uses the existing process experiment data input by the experiment module to automatically identify the nonlinear process dynamic model, checks the quality of the model, and adjusts it according to the quality of the model The signal is input to the experiment module to adjust the current experiment parameters. The invention can carry out identification experiment and model identification on the nonlinear industrial process, and the nonlinear industrial process can be continuous, intermittent or feeding intermittent. The obtained nonlinear process dynamic model can be used in model predictive controllers, conventional PID (proportional, integral and differential) controllers and other advanced process controllers, and can also be used in reasoning models and soft sensors for product quality prediction .
Owner:朱豫才
System and method for acquiring user data in user equipment based on soft sensor
ActiveCN110990239AHigh precisionRealize the collectionHardware monitoringEnergy efficient computingData classData set
The invention discloses a system and method for collecting user data in user equipment based on a soft sensor. The system comprises an initialization device which carries out the analysis of a presetconfiguration file, so as to determine a plurality of data types of the user data needing to be collected; the sensor generation device is used for generating the plurality of soft sensors and carrying out time continuous collection on the operation data of the respective corresponding data types by utilizing each soft sensor; the data sampling device is used for determining each sampling point from the operation data continuously acquired by each soft sensor according to the respective sampling time interval and taking a data set of the operation data at each sampling point as a sampling dataset of each soft sensor; and the data processing device is used for determining the sampled data set subjected to data filtering of each soft sensor as the acquired user data. According to the invention, the behavior data of the user for the rich media can be collected, and the space scale of log reporting can be reduced.
Owner:北京思维造物信息科技股份有限公司
Sensing Systems
PendingUS20200255791A1Reduce maintenanceLow costBioreactor/fermenter combinationsBiological substance pretreatmentsSensor arrayMicrofabrication
A thin, soft sensor array system that can be deployed over the surfaces of bag bioreactors. The sensor array is fabricated using microfabrication processes along with functionalization methods necessary for measuring pH, glucose, and temperature. Miniature integrated circuit (IC) components are incorporated with the thin-film circuits, allowing for the real-time, on-board data analysis and wireless data communication.
Owner:GEORGIA TECH RES CORP
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