97 results about "Control chart" patented technology

The invention discloses a road structure performance catastrophe early warning method and system. The method comprises the following steps: S1, synchronously acquiring data by using a three-dimensional ground penetrating radar and a deflectometer; s2, introducing a dielectric constant correction signal, quantifying a volume defect, and taking the volume defect as a physical constraint inversion mechanical parameter; s3, fusing the multi-dimensional parameters to construct a damage potential index, and predicting the residual life by using a graph space-time neural network; and S4, on-line mutation detection is carried out based on a Bayesian and cumulative sum control chart algorithm, and the weight of the model is updated through a treatment feedback closed loop. According to the method, physical-data dual-drive modeling is adopted, the problems of one-sided detection means, poor model interpretability and parameter solidification are solved, and accurate evaluation of road performance degradation and continuous evolution of the model are achieved.

The invention relates to the technical field of industrial equipment monitoring, in particular to an MVR (mechanical vapor recompression) evaporator operation monitoring method for industrial wastewater treatment, which is capable of immunizing normal working condition amplitude interference and dynamically fusing a multi-sensor waveform mode, and comprises the following steps: S1, obtaining a normalized mode sequence of each sensor; s2, calculating a mode offset score between the normalized mode sequence and each corresponding health mode template; s3, performing weighted fusion on the mode offset score of each sensor based on the dynamic sensitivity weight to obtain a self-adaptive blockage index; and S4, analyzing a continuous deviation trend of the adaptive blockage index relative to a preset healthy baseline based on the cumulative sum control chart so as to monitor the running state of the MVR evaporator. According to the invention, the monitoring sensitivity and decision reliability of early-stage collaborative abnormity of blockage can be improved.

The invention discloses a transformer substation equipment defect image generation method and system based on two-stage denoising, belongs to the technical field of computer vision and image generation, and solves the problem that in the prior art, the geometry and contour of equipment cannot be still kept in a steady state under the condition that an inference end does not depend on a control chart. The method comprises the following steps: acquiring normal samples and defect samples of a substation scene target defect category, extracting structure priori in the normal samples, and selecting a diffusion base model at the same time; in the first stage, a diffusion model base and a lorabase model are fused to generate latent variables with residual noise, and in the second stage, a diffusion base model, the lorabase model and a loradeject model are fused to continue decoding the latent variables with the residual noise to generate defect images; and based on the training set B and a defect image training discrimination model, discriminating the defect image, and screening generated defect data which cannot be distinguished from real distribution, namely generating a final substation equipment defect image. The method is used for generating the transformer substation equipment defect image with two-stage denoising.

The invention discloses a cross-domain data collaborative management method based on edge computing. The method comprises the following steps: S1, acquiring data to form a time series data set; s2, extracting features of time sequence data at edge nodes, and generating a state vector sequence; s3, inputting the state vector sequence into an edge decision network for weighted aggregation to generate a collaborative state sequence; s4, detecting a behavior change point of the collaborative state sequence by adopting a cumulative sum control chart method, and constructing a state mapping sequence; s5, based on the state mapping sequence, constructing a region index set through discrete wavelet transform; s6, inputting the regional index set into an edge decision network to generate a management instruction sequence; and S7, executing the management instruction sequence at the edge node, and updating the edge decision network based on an execution result. According to the method, multiple intelligent algorithms are fused to realize data collaborative management of cross-domain edge nodes, and the response efficiency and decision-making ability in an edge environment are improved.

According to the temperature control and SPC statistical system for continuous cyclic cap making, temperature detection focuses on a threaded area on the side edge of a bottle cap, and a temperature data-bottle cap-mold cavity number association is established in combination with a groove of a forming device and double proximity switches; an upper die and lower die two-way independent cooling loop is adopted, the on-off proportion of an electromagnetic valve is controlled and adjusted according to the periodic circulation time proportion, and pressure balance is achieved in cooperation with a cold water bypass electromagnetic valve. An SPC statistical method is introduced, temperature fluctuation and mold cavity consistency are monitored through a control chart after data preprocessing, graded early warning is generated, and cooling parameters are dynamically optimized through a waterway optimization closed-loop unit. Accurate temperature control, abnormity traceability and mold cooling dynamic regulation and control of the key area of the bottle cap are achieved, the product percent of pass and production stability are improved, and energy consumption and maintenance cost are reduced.

The application provides an anti-noise signal periodicity anomaly monitoring method and system which overcomes correlation false positives, comprising: a collection step: collecting data signals every preset time interval; a processing step: dividing the collected data signals according to a preset rule and performing feature extraction; a conversion step: performing orthogonal conversion on the extracted features to eliminate the correlation between the features; a monitoring step: processing the converted data to form an MO control chart, and monitoring the MO control chart; and if abnormal data appears, reporting. The MO test based on the EnNRC features can maintain high test accuracy under strong background noise and has stronger anti-noise interference ability.

The application discloses a flexible circuit board production quality monitoring method and system based on data feedback, collects physical parameters such as line width, line spacing, hole diameter, roundness and copper thickness of developing etching, drilling and copper plating processes, and workshop temperature and humidity and dust concentration data, generates a single variable control chart for single process physical parameters, generates a multivariate T 2 Control chart for multiple coupling parameter processes, calculates process stability index and comprehensive fluctuation index, inputs abnormal data into a BP neural network, identifies abnormal mode types and outputs characteristic parameters, generates parameter correction instructions according to the abnormal mode and the characteristic parameters, adjusts equipment parameters in real time, recalculates the process capability index based on the adjusted data, triggers secondary feedback if the process capability index does not meet the standard, and dynamically adjusts the control chart threshold value according to the standard deviation and mean value of the process capability index of continuous batches. The application comprehensively covers key production parameters, discovers abnormalities in time, accurately and comprehensively detects, adjusts in real time to ensure stability and controllability, and improves product quality.

The embodiment of the application provides a label verification method and device, an electronic device and a storage medium, and belongs to the technical field of financial technology. The method comprises the following steps: obtaining a data label to be verified; classifying the data label, and screening out a target data label of a numerical type; inputting the target data label into a preset decile label model in batches to generate a verification result of the target data label; generating a data control chart based on the verification result; and performing rationality judgment on the data distribution of the target data label according to the data control chart and a preset threshold range to obtain a judgment result, wherein the judgment result comprises whether the target data label is an abnormal data label. Based on this, the embodiment of the application can quickly, efficiently and accurately verify customer labels, so that insurance business personnel can analyze the characteristics of customers from multiple angles, select customers according to the labels, and thus realize accurate customer touch and provide more refined operation targets for customers.

The application discloses a beat control method and device, a linear motor equipment and an industrial automation system, and belongs to the technical field of automation control. The method comprises the following steps: after starting a program corresponding to a beat control graph element, attribute parameters of the beat control graph element are acquired; the attribute parameters are set through an interactive interface, and the attribute parameters are determined according to operation equipment beat information and operation equipment distribution information; arrival position information and cooperation time interval information corresponding to at least one working procedure are determined according to the attribute parameters, so that a mover component is controlled to move to a position indicated by the arrival position information within a time interval indicated by the cooperation time interval information. Compared with the prior art, the beat control strategy is adjusted by modifying codes, and the method has the advantages of being more intuitive and convenient to operate, high in debugging efficiency and strong in universality.

The invention discloses a tobacco process whole flow monitoring method, device and equipment, a medium and a product. The method comprises the steps that in a data processing platform, a central database is connected, to-be-analyzed data are obtained, and the to-be-analyzed data are acquired in real time through Internet of Things equipment of a plurality of to-be-monitored nodes deployed in the whole process of the tobacco technology; performing abnormal data monitoring on the to-be-analyzed data through a control chart theory or a machine learning algorithm, determining an abnormal data monitoring result, and visually displaying a related monitoring chart of the to-be-analyzed data; under the condition that the abnormal data monitoring result indicates that the abnormal data does not exist, optimizing the set process parameters corresponding to the to-be-analyzed data through a deep learning algorithm in combination with a feedback controller, and continuing the process flow through the optimized set process parameters; and when the abnormal data monitoring result indicates that the abnormal data exists, performing abnormal data early warning. According to the scheme, high efficiency and stable quality of related production processes of the tobacco process can be ensured.

The invention provides an electric submersible screw pump rotating speed adjusting method and system considering crude oil viscosity. The method comprises the following steps: calculating the power consumption per ton of liquid per hundred meters by utilizing the working fluid level depth, the oil pressure, the casing pressure, the pump depth, the daily power consumption of a single well and the daily produced liquid; determining the maximum reasonable submergence under the predefined crude oil viscosity and the minimum reasonable submergence under the predefined viscosity according to the correlation between the data of the hectometer ton liquid power consumption and the submergence data, and drawing a block critical submergence control chart; and searching a block critical submergence control chart, and adjusting the rotating speed of the electric submersible screw pump according to whether the actual submergence is between the maximum reasonable submergence and the minimum reasonable submergence. According to the scheme provided by the invention, the comprehensive influence of the crude oil viscosity on the electric submersible screw pump, shaft flow and other systems can be considered, the critical submergence control chart is established, different submergence is recommended for oil wells with different crude oil viscosities, differential supply and production balance is realized, the overall lifting efficiency of a block is improved, and benefit development is realized.

A method can include, responsive to actuation of a control graphic of a rig control framework, selecting an identifier for a well; responsive to the selecting, automatically accessing a digital file associated with the identifier; responsive to the accessing, automatically extracting planned drilling parameters for the well from the digital file; during drilling of a length of the well, acquiring real-time data via the rig control framework; and, responsive to the acquiring real-time data for the length of the well, automatically generating depth-based chart graphics via the rig control framework, where the depth-based chart graphics include statistical distributions for actual drilling parameters based on the real-time data and indicia based on the planned drilling parameters.

The application discloses a low-gas mine gas emission abnormal source precision detection and treatment method, which is used for solving the problem of low-gas mine gas abnormal emission. First, the monitoring points and the monitoring point positions are determined, and all gas monitoring data are obtained through a mine monitoring system; data analysis is carried out based on SPC control chart theory, and a gas emission data anomaly criterion is established; the gas emission state is judged through the SPC control chart method, the gas emission abnormal area position is determined, and early warning is carried out; according to the determined gas emission abnormal area, the isotopic analysis method is used to detect the gas emission source and composition; finally, the gas emission abnormal source analysis result is tracked, targeted gas source classification treatment measures are formulated, and then the treatment effect test is carried out. The application can effectively control the low-gas mine gas emission abnormal risk, thereby improving the safety of coal mining.

1. Name of the product in this design: Graphical User Interface for Controlling Rotary Dehumidifiers for Electronic Devices. 2. Application of this design: This design is intended for use in an electronic device. 3. The key design features of this product are its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The graphical user interface of the product is the software interface for the control and adjustment system of the rotary dehumidifier unit; the main view of Design 1 is the main interface of the software; the interface change state diagram of Design 1 is the interface displayed after clicking the "Parameter Query" option at the bottom of the main view interface of Design 1. Design 2 is a similar design. The main view of Design 2 is the main interface of the software. The interface change state diagram of Design 2 is the interface displayed after clicking the "Parameter Settings" option at the bottom of the main view interface of Design 2. Design 3 is a similar design. The main view of Design 3 is the main interface of the software. The interface change state diagram of Design 3 is the interface displayed after clicking the "Historical Alarms" option at the bottom of the main view of Design 3. Design 4 is a similar design. The main view of Design 4 is the main interface of the software. The interface change status diagram of Design 4 is the interface displayed after clicking the "Trend Chart" option at the bottom of the main view interface of Design 4. Design 5 is a similar design. The main view of Design 5 is the main interface of the software. The interface change state diagram of Design 5 is the interface displayed after clicking the "Factory Repair" option at the bottom of the main view of Design 5.

The invention discloses an intelligent infection risk monitoring and control system based on machine learning, and the system comprises a data collection and preprocessing module which is used for collecting multi-source environment data, and generating a multi-dimensional time series data set; the hierarchical Dirichlet working condition identification module is used for establishing a hierarchical Dirichlet working condition model and forming an infection risk working condition identification result; the dynamic control chart construction module is used for constructing a dynamic control chart and forming a dynamic topological structure; the multi-component coupling predictive control module is used for executing predictive control operation and outputting a control execution result; the self-evolution memory potential field control module is used for calculating potential field energy and updating a control target weight; and the optimal control and feedback module is used for generating and executing an optimal control instruction and feeding back an execution result. By fusing hierarchical Dirichlet identification, dynamic prediction control and self-evolution potential field adjustment, real-time monitoring and self-adaptive control of infection risks are realized.

This invention relates to an integrated method for bearing condition monitoring and fault diagnosis under complex noise conditions, belonging to the field of mechanical equipment condition monitoring and fault diagnosis. This method extends the classic noise-resistant correlation (NRC) method to resist interference from complex noise. The extended NRC is fused with the frequency domain energy ratio to form a composite correlation coefficient. Integrating the composite correlation coefficient through a sliding window yields a health characteristic index that monotonically changes with bearing degradation. Subsequently, an exponentially weighted moving average (EWMA) control chart is used to track this index to indicate early anomalies. Simultaneously, this method highlights fault-related periodic signals while achieving monitoring, realizing the integration of monitoring and diagnosis.

1. Name of the product in this design: Graphical User Interface for Controlling Collision Test Data of Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 main view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: The graphical user interface in this design is used to view collision test data. 7. Description of the changing states of the graphical user interface: The main view of Design 1-Design 5 is the graphical interface for controlling and viewing the collision test data of the robotic arm. The top of the interface features a collision scheme selection area with a selection bar and operation buttons on the right. The middle of the interface displays the collision relationship. Users can navigate to different pages by clicking the operation buttons displayed on the right side of the selection bar.

This invention relates to the field of data processing technology, and more specifically, to an intelligent processing method for multi-source monitoring data of high-risk chemical products. The method includes: acquiring the concentration sequence of high-risk chemical gases and synchronous micrometeorological parameters within the target monitoring area; constructing an environmental non-stationarity intensity based on the characteristics of the micrometeorological parameters; calculating a baseline drift tendency coefficient by combining historical trends to assess the probability that concentration changes are attributable to environmental drift; dynamically correcting the background baseline using the baseline drift tendency coefficient to generate net leakage characteristics; and introducing the net leakage characteristics into a cumulative sum control chart algorithm to construct an adaptive damping cumulative quantity to achieve leakage monitoring of high-risk chemical products. This invention accurately assesses the impact of micrometeorology on background concentrations, scientifically distinguishes between environmental drift and actual leakage, effectively eliminates interference and reduces false alarms, dynamically adjusts the cumulative intensity to ensure stable detection under different environments, improves monitoring accuracy and reliability, and provides efficient protection for chemical safety.

A process for installing lockers includes manufacturing a plurality of combination locks, wherein each combination lock includes a first scannable feature. The locks are installed in multiple locker assemblies. The locker assemblies each include a second scannable feature uniquely identifying the locker assembly. Each combination lock is associated with a corresponding unique serial number and unique combination. The unique serial numbers and unique combinations are stored in a database. The locker assemblies are provided to a locker installer and the locker installer installs the locker assemblies in a location. The first scannable feature is scanned using a scanner and a unique indicia of the combination lock is identified using the first scannable feature. A control chart is created by providing a scanner output to a remote computer system storing the database. The control chart associates each locker with a combination of the combination lock installed in each locker.

The invention provides an SPC quality management method and device based on model driving and a storage medium. The method comprises the steps that data collected in all stages in the production process is stored in a document type database; starting an SPC quality management service by adopting a triggering mechanism; extracting data from the document database, and cleaning the extracted data based on a pre-configured conversion model to generate a data analysis model; on the basis of the data analysis model and a pre-configured point location algorithm model, point location models of multiple SPC control charts are generated; and calling a pre-configured early warning model to carry out early warning monitoring on point locations in the point location models of the multiple SPC control charts, and generating an abnormality judgment report when abnormal conditions are monitored. Large-scale data can be efficiently processed, and the system performance and the data processing efficiency of the server can be improved.

The invention discloses an SPC quality modeling method, device and equipment, a medium and a product, and belongs to the technical field of production quality control, and the method comprises the steps: obtaining a sampling value of a target parameter in a specified time range under a target process and the specification upper and lower limits of the target parameter; calculating a capability index of the target parameter in a specified time range according to the sampling value of the target parameter and the upper and lower limits of the specification; and displaying a header by adopting an Element UI according to the capability index, and drawing a control chart reflecting the stability and capability of the production process by adopting Echart. According to the method, key indexes in the production process are monitored in real time, a statistical method is used for analyzing data, and production deviation or potential quality problems are found in time, so that accurate control over the production process is achieved, and the production quality and process stability of the lithium battery are improved.

This invention discloses a power quality anomaly tracing method based on multivariate statistical decoupling, comprising: acquiring historical power quality data and relevant data at the time of analysis, and processing the data; identifying overall anomalies based on Hotelling T² control charts; if an overall anomaly is identified, performing anomaly statistical decoupling analysis based on Mason decomposition, and obtaining the order conditional statistics, unconditional statistics, and complete conditional statistics of each node, and determining whether each statistic reaches a significance level; when both the unconditional and complete conditional statistics of a node are significant, the node is the root cause; when the unconditional statistics of a node are not significant but the complete conditional statistics are significant, the node is a relational anomaly; when the unconditional statistics of a node are significant but the complete conditional statistics are not significant, the node is a cascading phenomenon; this invention improves the location efficiency, analysis accuracy, and decision-making pertinence of power quality operation and maintenance in distribution networks.

This invention relates to vibration data detection technology and discloses a vibration data trend anomaly detection algorithm based on OAKR-MEWMA fusion, comprising: data preprocessing: the raw vibration data is first standardized to eliminate dimensional differences and ensure input consistency; data transmission: the preprocessed data is input in parallel to two core modules: an online adaptive kernel regression module and an improved multivariate exponential weighted moving average control chart module; anomaly score output: the online adaptive kernel regression module captures nonlinear anomaly features through kernel function mapping. The technical problem this invention aims to solve is to provide a vibration data trend anomaly detection algorithm based on OAKR-MEWMA fusion. By constructing an OAKR-MEWMA dual-channel fusion architecture and combining it with a quantile normalization strategy, the difficulty of fusing heterogeneous anomaly scores is successfully solved, achieving simultaneous capture of sudden anomalies and gradual degradation trends. The constructed multi-dimensional trend quantification index system realizes a leap from qualitative alarm to quantitative analysis.

The application discloses a kind of based on multi-sensor cooperation's tunnel multi-modal intelligent detection method, comprising: through the multi-dimensional original detection data set of tunnel structure surface, geological body inside and environment state related data of multiple sensing devices collected by tunnel global multi-sensing cooperation intelligent detection early warning platform deployment, call geological texture door control chart attention network extraction and enhance geological texture feature, adopt tunnel section deformation inversion algorithm to analyze section collection data to characterize section deformation state, utilize radar image texture reconstruction algorithm to process radar detection data, restore tunnel concealed part structure state.Processed multi-modal feature data is fused and associated, constructs global detection feature space, and outputs tunnel structure state detection result and abnormal signal identification after platform analysis and judgment.The application realizes the globalization, precision and intelligentization of tunnel detection, and provides reliable technical support for tunnel structure health monitoring.

The application discloses a time positioning method for gradual change noise of bridge health monitoring data, and comprises the following steps: 1) obtaining bridge health monitoring time series data without gradual change noise, and calculating the difference of original data; 2) calculating the mean square deviation of the difference sequence with 30s as a time unit, and obtaining the mean square deviation sequence; 3) calculating the change rate of the mean square deviation sequence, and calculating the mean and variance of the change rate sequence, and defining the mean square deviation change rate index I; 4) determining the upper and lower limits of the control chart, and the sample exceeding the upper and lower limits of the control chart indicates that the gradual change noise appears, so that the positioning of the gradual change noise is realized. The original complete monitoring data set containing short-time strong noise is obtained through the bridge health monitoring system, the positioning of the gradual change noise is realized based on the characteristics that the fluctuation of the noise is larger than that of normal data, and the gradual change noise data can be effectively positioned for subsequent cleaning.

The invention discloses an array type steam heat setting process optimization method based on machine learning. The method comprises the following steps: step 1, constructing a disturbance voxel map; 2, constructing a regional disturbance response matrix; 3, generating a candidate micro-intervention control unit set; 4, generating an area control intention vector based on the current cloth operation state; step 5, executing auction scheduling, bidding each region according to the control intention vector, determining a bid-winning region by adopting a Veckie auction mechanism, and constructing a bid-winning control map; sixthly, strategy re-parameter sampling is carried out according to the bid winning control atlas; 7, when the cloth cover is in an abnormal state, backtracking the bid-winning control atlas, and extracting an intervention control unit sequence; and step 8, adjusting the regional control intention vector and the bidding sequence based on the intervention control unit sequence, and forming a new bid winning control atlas. According to the method, voxel modeling and a Vicrie auction mechanism are fused, and intelligent optimization of the array type steam heat setting process is achieved.

The invention relates to the technical field of software updating, and provides a software upgrading risk prediction method, which comprises the following steps of: obtaining high-dimensional feature vectors corresponding to a to-be-evaluated software version and a plurality of historical software versions respectively, determining a sample covariance matrix according to the high-dimensional feature vectors corresponding to the plurality of historical software versions, and determining the risk of software upgrading according to the sample covariance matrix; according to the high-dimensional feature vector corresponding to the to-be-evaluated software version, the sample covariance matrix and the high-dimensional feature vector corresponding to the current to-be-upgraded software version, determining the mahalanobis distance between the to-be-evaluated software version and the to-be-upgraded software version, and for the multiple high-dimensional feature vectors, determining the to-be-upgraded software version according to the mahalanobis distance through an accumulation and control chart algorithm. And determining global mutation indexes corresponding to the to-be-evaluated software version and the plurality of historical software versions, and determining a risk evaluation index between the to-be-evaluated software version and the to-be-upgraded software version according to the mahalanobis distance and the plurality of global mutation indexes. The accuracy of risk assessment indexes in the software upgrading process is improved, and the accuracy of risk prediction is effectively improved.

The invention relates to the technical field of data processing, provides a server host intrusion behavior detection method, device and equipment and a medium, and is applied to financial and medical health care service scenes. The initial normal behavior panoramic feature map is constructed based on the to-be-processed log data, so that scattered log data can be converted into structured graph data, and a comprehensive data model is provided for subsequent detection; on one hand, the survival time of each node is configured, and the amount of graph data can be effectively controlled while the valuable data is kept; and on the other hand, after the perfection period is finished, aggregation statistics is performed on the abnormal events based on a sliding time window mechanism, and whether an intrusion risk exists or not is detected according to an aggregation statistical value, so that the false alarm rate of accidental events can be reduced while abnormal behavior detection is maintained, unknown threats and newly-added attack behaviors are effectively identified, and the detection accuracy is improved. Therefore, the detection accuracy is improved.

This disclosure provides an anomaly detection method, apparatus, electronic device, and storage medium. The method includes: dividing a continuous integration task with a task completion time within a first detection period into at least one task subgroup; for any task subgroup, determining the indicator value of the task subgroup for a target task indicator based on the task information of the continuous integration tasks within the task subgroup; performing anomaly detection on the indicator value of the task subgroup based on the control chart baseline value of the target task indicator within the first detection period and preset anomaly detection conditions to obtain an anomaly detection result for the first detection period; the anomaly detection result is used to indicate whether an anomaly exists and the anomaly level when an anomaly exists; and, if the anomaly detection result indicates the existence of an anomaly, performing an anomaly response processing corresponding to the anomaly level of the anomaly detection result. Embodiments of this disclosure can improve the accuracy of anomaly identification.

The invention discloses a monitoring method and device for quality data of a self-correlation mixing process, and relates to the technical field of disease monitoring. The method comprises the following steps: obtaining a group of multivariate self-correlation data in a historical controlled state and a group of multivariate self-correlation data in a historical out-of-control state; the method comprises the following steps: respectively calculating a sample mean value and a covariance matrix, carrying out decorrelation on samples through a Cholesky decomposition method, constructing an MEWMA sequence by utilizing standard independent data after decorrelation, training a classifier model of a random forest, designing a control chart method based on the random forest, and monitoring a linear autocorrelation process; on the basis of data decorrelation, according to the random forest theory, a novel control chart is generated, the modeling and monitoring problems of quality data in the self-correlation mixing process are effectively solved, the trained random forest model automatically learns and captures complex and nonlinear modes of data in controlled and out-of-control states, and the quality of the self-correlation mixing process is improved. And effective modeling and monitoring can be carried out no matter the data is in skewed, multi-peak or completely unknown distribution.