888 results about "Smart transducer" patented technology

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention relates to the technical field of sensor calibration, in particular to an environment adaptive multi-dimensional calibration method for an excavation type intelligent sensor, which comprises the following steps: acquiring temperature, humidity and gas data through environment monitoring, comparing with a threshold value, marking an abnormal state, evaluating the influence of each parameter on a sensor signal and analyzing fluctuation amplitude and intensity; combining sensor sensitivity and response rate to screen key factors, extracting factor fluctuation trend, optimizing signal disturbance weight and response path, correcting signals in real time, updating a calibration parameter set, monitoring signal values based on multi-dimensional parameters, and adjusting output to maintain a stable range. According to the invention, temperature and humidity and gas abnormity are dynamically identified through threshold comparison, a factor model is constructed based on sensitivity weight, a multi-dimensional calibration path is generated, a parameter set is updated in real time to decouple environment sudden change and sensor drift, signal self-adaptive convergence is realized through closed-loop control, multi-parameter interference errors are reduced, and the bottleneck of calibration delay is broken through. And the time-varying working condition data stability is ensured.

The invention discloses a hydraulic engineering seepage intelligent monitoring system and method, and belongs to the technical field of hydraulic engineering safety monitoring. The system comprises the following modules: a multi-source data acquisition module used for acquiring multiple types of monitoring data in real time through an intelligent sensor network; the double-window time sequence analysis module is used for constructing a quick response window and a trend analysis window to realize double identification of sudden anomalies and long-term trends; the self-learning threshold optimization module is used for automatically extracting a key quantile threshold based on the distribution characteristics of the monitoring data and continuously optimizing weight configuration and early warning threshold setting of various statistical indexes; the multi-scale fusion early warning module is used for performing multi-source information fusion, generating a comprehensive change index and a multi-stage early warning state, and outputting a seepage abnormity early warning signal and a corresponding confidence coefficient; and the visual decision support module provides visual data display, emergency response guidance and intelligent decision support.

The invention relates to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering operation risk monitoring management system based on multi-sensor fusion. The system comprises a data acquisition module, a data fusion module, an early warning and instruction generation module, and an instruction distribution and verification and correction module. The data acquisition module acquires structural deformation, water flow pressure and environment temperature and humidity data in real time through multiple types of intelligent sensors; the data fusion module inputs the collected data into a dynamic weighted fusion model, and generates a comprehensive monitoring index in combination with a sensor confidence coefficient weight and a historical data deviation rate; the early warning and instruction generation module triggers multi-stage early warning and generates an equipment regulation and control instruction set according to the comprehensive monitoring index and a preset safety threshold value; the instruction distribution module distributes the instruction to an execution terminal through a distributed message queue; and the verification and correction module checks terminal feedback signals by using a back propagation verification mechanism, and corrects the confidence coefficient weight of the model.

The invention discloses an online AOI detection system based on an industrial intelligent sensor, and the system comprises an image collection and preprocessing module which is used for collecting and preprocessing image data of an industrial product; the mask auto-encoder modeling module is used for constructing a mask auto-encoder model; the structure parameter optimization module is used for optimizing the mask auto-encoder model; the image reconstruction and difference extraction module is used for generating a reconstructed image, extracting an image difference region and determining a candidate defect region; the defect identification module is used for carrying out edge extraction and aggregation analysis, identifying a final defect area and acquiring spatial position information; the defect classification and labeling module is used for extracting defect area features and generating corresponding classification labels and grade labels; and the control response module is used for generating a control instruction and issuing the control instruction to the production line control device. According to the method, the high-precision automatic identification and real-time classification processing of the surface defects of the industrial product are realized by fusing the mask auto-encoder and the Tiancattle herd optimization algorithm.

The invention provides a full-link electricity consumption monitoring method, system and equipment based on intelligent Internet of Things, and relates to the technical field of power system management. The method comprises the following steps: acquiring operation data acquired by an intelligent sensor group deployed at a power network node in real time, and executing localized carbon flow accounting according to the operation data through an edge computing node; constructing a joint probability model based on Monte Carlo simulation and a deep belief network, and generating a node carbon flow density matrix under multiple scenes; dynamically updating an electricity-carbon conversion coefficient according to real-time energy structure data, and calculating node-level carbon emission: optimizing a power grid topology and an energy storage scheduling strategy by adopting a double-delay depth deterministic strategy gradient algorithm TD3 and taking a carbon flow density matrix as a constraint condition; and generating an intelligent report including a carbon footprint thermodynamic diagram, emission reduction potential evaluation and block chain evidence storage. According to the invention, the capability of multi-target collaborative optimization of the security and economy of the power grid can be improved.

The technical scheme of the invention discloses a photovoltaic power generation and greenhouse agriculture integrated system. According to the photovoltaic agricultural integrated technology disclosed by the invention, photovoltaic power generation and greenhouse planting are innovatively combined, and a solar panel with adjustable light transmittance of 30%-90% is mounted at the top of an agricultural facility, so that sunlight is utilized for power generation, and illumination required by crop growth is reserved. The system disclosed by the invention is provided with an intelligent sensor and an AI algorithm, environment data is monitored in real time, the angle, light supplementing intensity, temperature and humidity of the photovoltaic panel are dynamically adjusted, and different crop requirements (such as low illumination of leaf vegetables and medium and strong illumination of fruits and vegetables) are accurately matched. Residual electricity is stored or connected to a grid, photovoltaic waste heat is recycled in winter for heat preservation, sun shading and cooling are conducted in summer, and efficient circulation of energy is achieved.

The invention discloses a cable fault accurate positioning system based on an intelligent sensor system, and relates to the technical field of power fault positioning, and the cable fault accurate positioning system comprises a cloud monitoring center which is in communication connection with the following modules: a signal acquisition and processing module which is used for deploying the intelligent sensor system along a cable, operation environment data and traveling wave signals in the cable operation process are obtained, and the signals are preprocessed and stored in a cloud data warehouse. According to the invention, the front band-pass filter and the self-adaptive trap filter are combined to filter out high-frequency harmonic waves and specific-frequency interference signals, and the differential sampling technology is adopted to counteract common-mode interference, so that the signal-to-noise ratio of the signals is remarkably improved, a system can accurately capture and process weak traveling wave signals caused by a high-resistance grounding fault in a complex environment, and the fault detection accuracy is improved. Therefore, the fault positioning precision is greatly improved, the positioning error caused by signal interference is reduced, and the determination of the fault position is more reliable and accurate.

The invention relates to the technical field of laser heat treatment, and provides a calibration method of a full-automatic circuit board cutting machine. The method comprises the following steps: acquiring physical coordinates of a circuit board of a cutting device through an intelligent sensor, and constructing a reference coordinate system matrix based on the physical coordinates; determining vibration parameters of the cutting device according to coordinates of laser spots emitted by a laser and the reference coordinate system matrix, and generating a nonlinear error component according to the vibration parameters and power information of the laser; generating an error function based on an error matrix formed by the error components, and determining a calibration coefficient vector by solving the error function; closed-loop control is conducted on the laser path of the cutting device through the calibration coefficient vector, the adaptability of the cutting device to complex working conditions is improved by integrating multi-source error factors and establishing a nonlinear mapping relation, and the cutting precision and stability are ensured.

The invention discloses a settlement monitoring system based on computer vision, and relates to the technical field of infrastructure monitoring, the settlement monitoring system comprises a settlement monitoring platform, the settlement monitoring platform is in communication connection with an image acquisition module, a sensor network module, a data fusion analysis module, a settlement abnormity identification module and a settlement early warning module, the modules are in electric signal connection; and the image acquisition module is used for monitoring a foundation area of the infrastructure by using a plurality of cameras deployed in a monitoring area to obtain image data of the infrastructure. Through combination of the image acquisition module and the sensor network module, comprehensive coverage and accurate monitoring of an infrastructure foundation area are realized, a plurality of cameras are utilized to carry out omnibearing and multi-angle image acquisition, dead-corner-free monitoring is ensured, physical parameter data of a foundation are acquired in real time through multiple types of intelligent sensors, and the monitoring precision is improved. Monitoring comprehensiveness and accuracy are obviously improved, and potential safety hazards caused by monitoring blind areas are effectively avoided.

The invention provides an intelligent fault diagnosis method and system for a photovoltaic system. The method comprises the following steps: collecting multi-source heterogeneous operation parameter data of the photovoltaic system based on an intelligent sensor network and carrying out standardized preprocessing; local feature extraction is carried out on edge nodes, a frequency moment is calculated, data summary aggregation is carried out through a frequency moment optimization algorithm, and a fusion key feature vector set is constructed by using a multi-modal data fusion model; performing anomaly detection based on the normal behavior mode baseline, and generating an abnormal state alarm signal and a preliminary anomaly type indication; constructing a weighted graph model based on a system topological structure, constructing a fault association sub-graph by using an approximate spanning tree algorithm, accurately positioning a fault unit and diagnosing a fault type; and generating and executing a self-healing operation scheme according to the fault diagnosis conclusion. Through the distributed multi-modal data fusion and approximate spanning tree fault positioning technology, rapid and accurate diagnosis and autonomous recovery of the photovoltaic system fault are realized, and the system reliability and the operation efficiency are improved.

The invention discloses a man-machine interaction method and system based on an intelligent sensor, belongs to the technical field of robots, solves the problem of misoperation caused by abnormal sensor data to a certain extent, and enables the system to still run stably through quality credibility evaluation and a dynamic weight distribution mechanism when part of sensors fail. Meanwhile, based on a dynamic safety boundary strategy of scene semantic understanding, the robot can automatically adjust motion constraints according to object attributes and spatial relations, and collaborative optimization of safety and operation efficiency is achieved in complex tasks.

The invention discloses an intelligent monitoring system for full life cycle aging of equipment based on Internet of Things, and relates to the technical field of Internet of Things equipment monitoring. Original analog signals are collected through an intelligent sensor, and conditioned analog signals are output by using a built-in adaptive gain conditioning circuit; converting the conditioned analog signal into a digital sequence through an adaptive analog-to-digital converter, carrying out dynamic sliding window filtering on the digital sequence, calculating derivative features according to the filtered digital sequence, combining original features with the derivative features to obtain a real-time feature vector, dividing the whole life cycle of the equipment into a running-in period, a stable period and a decline period, and carrying out dynamic sliding window filtering on the digital sequence; and constructing a feature state matrix of each stage, obtaining an equipment aging level based on the feature state matrix and the real-time feature vector, carrying out anomaly detection from three dimensions of time, space and features in combination with the equipment aging level, and executing different early warning strategies according to an analysis result. According to the invention, the accuracy of equipment life cycle monitoring is effectively improved.

A method to perform oil in produced water analysis allows measuring the large volume of oil in produced water reliably. In the method, a time-series and physics based machine learning model of a gas oil separation plant is generated, advisory actionable items for maintaining a crude oil quality within a pre-determined threshold are generated based on machine learning model coefficients and outputs of soft sensors, and then the advisory actionable items are presented to a user.

The invention relates to the technical field of cubic hydraulic press monitoring, in particular to a cubic hydraulic press health monitoring method and system based on an intelligent sensor, and the system comprises a health monitoring terminal, a data sensing module, a data processing module, a health diagnosis module, a fault management module, a diagnosis positioning module, a cooperative influence module and a rear-end execution module. According to the method, the time domain, the frequency domain and the time domain-frequency domain combined features are extracted, meanwhile, the state change slope is quantified, early warning of the equipment degradation trend is achieved, a fault type-abnormal feature-weight mapping table is constructed, a fault core component and the specific position are accurately positioned, and the fault core component is accurately determined by matching with digital twinborn visual marks. And meanwhile, the internal relation between components is quantified by constructing a component incidence matrix, the health influence coefficient of the cooperative component is calculated in combination with a fault grade coefficient, key components affected by faults are recognized in advance, a cooperative damage list is formed, and operation and maintenance personnel are assisted to intervene in advance.

The invention provides an intelligent sensor cooperative control method and system for a microbiological detection environment, and relates to the technical field of sensor control, and the method comprises the steps: carrying out the hierarchical cloud edge control architecture layout of a production workshop according to equipment distribution information and production activity information, and obtaining a cascaded hierarchical control network; the K-level cooperative control secondary center executes partition autonomous parameter mapping and outputs a K-layer distributed environment compensation strategy; the microbial environment cooperative control center carries out transition area buffer control and outputs a global cooperative control strategy; and the microbial environment cooperative control center issues a global cooperative control strategy to the environment stability maintaining equipment array based on the spatial topological relevance of the environment stability maintaining equipment, and low-fluctuation environment steady state maintenance is carried out. The technical problem that the stability of a microbiological detection environment is affected due to the fact that most microbiological detection environment adjusting systems in the prior art are based on fixed parameter setting and lack of a quick response mechanism for sudden environment changes is solved.

The invention discloses an error compensation method of a strain type six-dimensional force sensor based on multi-source sensing information fusion, and belongs to the technical field of intelligent sensors. The error compensation method comprises the following steps: data acquisition; preprocessing and fusing data; constructing an error prediction model by adopting a time sequence neural network; training data construction and model training; performing online error compensation and feedback; and verifying, adjusting and optimizing the system. Based on fusion of multiple sensors (IMU, a temperature module and a timer) and time sequence neural network modeling, combined online compensation of errors caused by gravity, temperature drift and time drift of the six-dimensional force sensor is achieved, the method has the advantages of being high in compensation precision, high in response speed and high in adaptive capacity, and the application requirements of various high-precision measurement and control systems are met.

The invention relates to the technical field of intelligent sensors, in particular to a workshop harmful gas abnormity monitoring method and system based on multi-modal data, and the method comprises the following steps: obtaining the multi-period concentration change of a node, recognizing the reverse direction, forming a bounce fragment, combining node behaviors according to the correlation of time and space, and constructing a diffusion path; and in combination with node toxicity residence and trend change and gas and time information at the convergence position, a workshop multi-modal toxicity anomaly monitoring result is obtained. According to the method, gas fluctuation characteristics are captured by recognizing the concentration change direction and the reversal amplitude, a behavior combination is constructed by combining time and space information, the linkage recognition capability between nodes is enhanced, a path sequence is constructed by depending on the trend direction and the node sequence to present a diffusion track, and extension and mutation nodes are extracted by superposing the residence state and the change trend. The dynamic monitoring range of toxic gas in multiple scenes is expanded, and correlation analysis of behavior traceability, path tracking and abnormal gathering areas is supported.

A method and system of providing guidance and information to an end-user during an emergency situation. A network of beacon devices is arranged throughout different rooms of a physical structure, such as a house. When an emergency situation such as a fire is detected, for example via data collected from one or more smart sensors installed in the house, a navigation server can activate the beacon device network. Each beacon device will begin to provide navigation cues such as audio content and / or light display that guide the end-user to the nearest designated exit.

The invention discloses a metal tube float flowmeter based on an intelligent sensor, and relates to the technical field of float flowmeters, the metal tube float flowmeter comprises a tube body and a detection mechanism, an instrument is fixed on the outer side of the tube body through a bolt and used for realizing flow display, the instrument is connected with a Hall element, the detection mechanism used for flow detection is installed in the tube body, and the Hall element is connected with the detection mechanism. And the locking mechanism is used for realizing initial locking of the detection mechanism. According to the metal tube float flowmeter based on the intelligent sensor, the elastic regulation and control mechanism is arranged at the position of the liquid inlet of the tube body, peak kinetic energy of fluid is actively absorbed through elastic deformation of the first spring, and therefore the fluid entering the tube body can be buffered, pulsating flow of the external input fluid is effectively converted into stable flow, and the flow rate of the fluid is increased. Jumping of the floater body is greatly reduced, the problem that reading is difficult due to jumping of the floater body can be solved, the positive error of an average measured value caused by pulsation can be avoided, and the accuracy of detection data is guaranteed.

The invention discloses a real-time adaptive temperature monitoring intelligent sensor based on a deep learning algorithm, and belongs to the technical field of environmental parameter measurement and artificial intelligence. The sensor is composed of a main temperature measuring unit, an auxiliary environment sensing unit, a data acquisition circuit, a microcontroller, a deep learning processing module, a memory, a communication module and a power supply module. Multi-modal data are obtained through the auxiliary environment sensing unit, and the multi-modal data are preprocessed and then input into the self-adaptive temperature sensing network. In the adaptive temperature sensing network, a multi-mode encoder extracts time sequence characteristics of each channel, a convolution auto-encoder reconstructs a normal temperature measurement mode and performs anomaly detection, and a fusion decoder fuses output of each sensor into a corrected temperature value based on a dynamic weight formula. The method has the advantages of high real-time performance, high measurement precision, timely anomaly detection, high self-learning capability, low power consumption and the like.

The invention discloses a building engineering construction quality progress monitoring method and system based on artificial intelligence. A BIM model is constructed according to image data and project drawings of an engineering site; according to the BIM model, intelligent sensors are arranged on site, and online construction information is collected; online construction information is extracted, three-dimensional environment scene reconstruction is carried out according to the surrounding environment, and construction progress data is obtained; calculating an environment fluctuation value according to the construction progress monitoring condition and the online construction information, and setting a construction adjustment strategy; constructing a gray scale prediction model, and predicting a progress critical value result according to the construction progress data and the construction period after construction adjustment; and the construction progress data, the three-dimensional environment scene and the progress critical value prediction result are visualized to generate a BIM simulation large screen, so that the scheduling scientization of the construction process is realized.

The invention discloses a monitoring feedback system for an embankment compaction process under high liquid limit soil based on an intelligent sensor, and relates to the technical field of high liquid limit soil roadbed construction, and the monitoring feedback system comprises an intelligent sensor node buried in embankment filling soil, a data communication module connected with the node, and a feedback control terminal for receiving data; the intelligent sensor node is characterized in that the intelligent sensor node comprises a dynamic self-calibration anchoring mechanism which is composed of a deformable memory alloy net-shaped anchoring sleeve, and the sleeve is in a contraction state during initial embedding, expands into a multidirectional barb net-shaped structure and is embedded into a soil body when the temperature is increased to a preset phase change temperature range through rolling. According to the monitoring and feedback system for the compaction process of the embankment under the high liquid limit soil based on the intelligent sensor, the problem of data distortion caused by displacement and inclination of an embedded sensor is solved; and through intelligent linkage of an anchoring program and compaction parameter feedback, full-closed-loop control of displacement suppression, data fidelity and failure repair is formed, and the accuracy of compaction state evaluation and the reliability of feedback regulation and control are ensured.

The invention discloses a preparation method of a flexible electromagnetic shielding type force sensor based on a coaxial structure, and belongs to the field of flexible electronics and intelligent sensors. According to the method, a coaxial wet spinning process is adopted, a core-sheath structure conductive fiber is constructed, a core layer is made of a multi-walled carbon nanotube (CNT) and thermoplastic polyurethane (TPU) composite material, and a sheath layer is made of a nickel-coated carbon nanotube (Ni-CNT) and TPU composite material. By regulating and controlling the proportion of the core sheath, the construction of the gradient conductive network is realized, and the sensing sensitivity, the electromagnetic shielding performance and the mechanical flexibility are improved. The obtained Ni-CNT-coated CNT fiber is made into fabric and integrated in a sensing glove, and multi-finger bending detection and wireless man-machine interaction control are achieved. The sensor has the advantages of high sensitivity, wide strain detection range and excellent electromagnetic shielding effectiveness, is suitable for the fields of intelligent wearing, anti-interference man-machine interface and fire-fighting equipment, and can also be expanded to complex application scenes such as aerospace teleoperation.

Systems, methods, and devices relate to a smart sensor usable for monitoring materials within container systems. A smart sensor device may include a base portion provided on an exterior of a tank containing a material, and an internal cavity housing a sensor to detect a level of the material within the tank through an outer surface of the container. The smart sensor device may track readings over time, from one or more container systems, and identify various usage trends. Usage trends and safety alerts may include current supply levels, historical usage rates, and low-level alerts. Such alerts and notifications may be communicated to a dashboard on a remote computing device.

The invention provides an electrocardiograph electromagnetic compatibility test data diagnosis method based on an intelligent sensor, and relates to the technical field of electrocardiograph electromagnetic compatibility test. The method includes generating structured test data. And calculating a theoretical interference reference threshold. And generating an initial interference judgment atlas arranged according to the interference time sequence. And constructing a dynamic compensation coefficient matrix, and injecting the dynamic compensation coefficient matrix into the initial interference determination map to obtain a dynamic interference determination map. And calculating the characteristic similarity between the real-time test data in each interference time sequence window and the dynamic interference judgment map, and diagnosing the electromagnetic compatibility state of the electrocardiograph. According to the invention, through multi-dimensional data acquisition and interference identification, through a dynamic reference threshold and a compensation mechanism, environment and equipment state changes are adapted, the diagnosis stability of a complex scene is improved, through real-time data comparison and traceability, the equipment state is rapidly judged, the interference source is positioned, the debugging time is shortened, and finally the measurement accuracy of the electrocardiograph is guaranteed.

The invention relates to the technical field of environmental monitoring of artificial intelligence, and particularly discloses a dangerous waste storage environment real-time monitoring and risk early warning system based on digital twinning, which collects multi-modal environmental data in real time through an intelligent sensor network deployed in a dangerous waste storage facility, and constructs a digital twinning model synchronized with a physical entity; a multi-modal data fusion and physical embedding technology is adopted, and virtual risk parameters of an area which is not actually measured are calculated based on an environment evolution rule; an abnormal association mode between the measured data and the virtual risk parameters is analyzed and identified through the dynamic association network, and risk level judgment and early warning signal triggering are achieved; a sensor monitoring strategy is dynamically adjusted according to an early warning result to form closed-loop management and control; according to the invention, the sensor limitation of the traditional monitoring system is broken through, the advanced accurate early warning of the hidden risk is realized, and the monitoring efficiency is obviously improved through adaptive resource configuration.

An artificial reality system performs local user adaptation of machine learning models and global improvement of the machine learning models while ensuring data security and privacy. An artificial reality headset uses smart sensors and a system on chip (SoC). The smart sensor extracts features from sensor data, encrypts them and transmits the encrypted features to the SoC of the headset. The SoC provides the encrypted features to a server, for example, a server in a cloud platform. The server updates a base model with collected features and sends the updated base model parameters to headsets. The smart sensors of a headset receiving the updated model parameters, balance the model parameters with locally stored feature data, for example, user calibration features to perform user customization of the machine learning model.

The invention provides a metering data automatic calibration method and system, and the method comprises the steps: obtaining an actual measurement output value of a target intelligent sensor for a measured physical quantity in a normal operation process of metering equipment, accessing a standard reference signal outputted by an external standard device subjected to the traceability of the quantity value, reading a pre-stored standard reference value and a standard threshold value, and carrying out the calibration of the measured physical quantity. According to the actually measured output value, the standard reference value and the standard reference signal, obtaining a standardized output value through a fitting mode; when the standardized output value exceeds a standard threshold value, switching to a calibration mode, providing forward and reverse standard input quantities by an external standard device, acquiring a response signal, and calculating a nonlinear compensation coefficient and a hysteresis correction coefficient; and updating the output correction parameter according to the compensation coefficient. By adopting the scheme of the invention, the cost constraint of different error attention in measurement data calibration can be realized, and the calibration robustness is improved.

The invention discloses a belt conveyor intelligent transportation method and system based on an intelligent sensor and electronic equipment, and the method comprises the steps: collecting key parameters from a belt conveyor through a multi-source heterogeneous intelligent sensor, including a state parameter of the belt conveyor, a state parameter of a belt and an environment change parameter; performing synchronous calibration, noise suppression and feature extraction on the multi-source heterogeneous data, and performing multi-source heterogeneous data fusion; constructing a belt conveyor operation multi-dimensional state vector based on the fused data; and inputting the state vector into a prediction model to obtain a future operation state prediction result. According to the method, the problems of'inaccurate prediction, insufficient advance and high missing report rate 'caused by'strong data isomerism, key feature weight deficiency, rare fault samples, insufficient model stability' and the like are solved.