488 results about "Fuzzy set" patented technology

A method and system are provided, which provides reliable fire detection. In one implementation, the automated system includes a combination of sensors configured to measure various factors associated with a hazard, such as a fire or gas leakage, and generate sensor readings. Factors measured can include smoke, carbon monoxide and heat. The system further includes a detection device that is configured to determine whether a hazard or fire exists by performing a fuzzy analysis of sensor readings. The fuzzy analysis includes categorizing respective sensor readings into fuzzy sets, and determining whether the hazard exists based on a combination of the categorizations. In addition the size and direction of a fire can be determined from multiple sensors.

The invention discloses a speech emotion recognition method. The method includes the steps that firstly, a speech signal is converted into a spectrogram to serve as initial input; secondly, a deep convolutional neural network is trained to automatically extract emotion features; thirdly, a stack type auto-encoder is trained for each kind of emotions, and all the stack type auto-encoders are fused to automatically construct membership functions of an emotion fuzzy set; fourthly, the features obtained in the second step are subjected to feature optimization by means of the fuzzy optimal theory in the third step; fifthly, emotion classification recognition is conducted by means of a Softmax classifier. The method takes abstract fuzzy properties of speech emotion information into consideration, the extracted emotion features are subjected to selective fuzzy optimization to improve the significance of the features, fuzzy membership functions in the fuzzy theory are automatically constructed by means of the concept of deep neural network layer-by-layer training, and the problem that the proper membership functions in the fuzzy theory are difficult to select and determine is solved.

The invention relates to a method for automatic fault diagnosis of a satellite and a diagnostic system thereof. The method comprises the following steps: dividing the satellite fault into a component level fault, a subsystem level fault and a system level fault; adopting a fault detection method based on a fuzzy set to fuzzify a precise sensor sampling value; defining different alarm weight values for fuzzy subset based on the membership grade belonging to each fuzzy subset; and determining whether to alarm or not by calculating the relation between the summation of the alarm weight values in a time window and an alarm threshold value. The method can effectively overcome the defects of a common fault detection method and effectively reduce the false alarm rate and the rate of missing report; and when certain fault is caused by a plurality of sub-faults, the method performs heuristic search according to the reliability of a system corresponding to each sub-fault and searches a sub-fault node corresponding to a system with low reliability preferentially. Besides, in order to adapt to the synergistic diagnosis between distributed satellites in the future, a synergistic interface is designed to provide diagnosis services for other satellites, thus the aim of the invention is achieved.

Methods and systems for a fuzzy logic engine (FLE) for an interactive product selector. In an embodiment, the interactive product selector provides a user with purchase decision questions (PDQs) regarding a product / service specified by the user. The PDQs can attempt to measure the user's product / service requirements. In an embodiment, the PDQs can be presented in radio button or check-box format, and as the user selects different options within the radio button or check-box PDQs, information is provided to the user and the options are recorded or otherwise tracked by the FLE. The FLE computes individual fuzzy sets for the PDQs according to the user's option chronology, and assigns membership grades to individual options. In an embodiment, the individual membership grades are associated with products / services based on feature, scaled, and thereafter averaged to compute a master fuzzy membership grade for a product / service. Higher master fuzzy membership grades can indicate a product / service more closely related to the user's requirements. The user can be presented with products / services in descending order of master fuzzy membership grade.

According to a first aspect of the invention there is provided a method of decision-making comprising: a data input step to input data from a plurality of first data sources into a first data bank, analysing said input data by means of a first adaptive artificial neural network (ANN), the neural network including a plurality of layers having at least an input layer, one or more hidden layers and an output layer, each layer comprising a plurality of interconnected neurons, the number of hidden neurons utilised being adaptive, the ANN determining the most important input data and defining therefrom a second ANN, deriving from the second ANN a plurality of Type-1 fuzzy sets for each first data source representing the data source, combining the Type-1 fuzzy sets to create Footprint of Uncertainty (FOU) for type-2 fuzzy sets, modelling the group decision of the combined first data sources; inputting data from a second data source, and assigning an aggregate score thereto, comparing the assigned aggregate score with a fuzzy set representing the group decision, and producing a decision therefrom. A method employing a developed ANN as defined in Claim 1 and extracting data from said ANN, the data used to learn the parameters of a normal Fuzzy Logic System (FLS).

The application borrows terminology from data mining, association rule learning and topology. A geometric structure represents a collection of concepts in a document set. The geometric structure has a high-frequency keyword set that co-occurs closely which represents a concept in a document set. Document analysis seeks to automate the understanding of knowledge representing the author's idea. Granular computing theory deals with rough sets and fuzzy sets. One of the key insights of rough set research is that selection of different sets of features or variables will yield different concept granulations. Here, as in elementary rough set theory, by “concept” we mean a set of entities that are indistinguishable or indiscernible to the observer (i.e., a simple concept), or a set of entities that is composed from such simple concepts (i.e., a complex concept).

A method and system are provided, which provides reliable fire detection. In one implementation, the automated system includes a combination of sensors configured to measure various factors associated with a hazard, such as a fire or gas leakage, and generate sensor readings. Factors measured can include smoke, carbon monoxide and heat. The system further includes a detection device that is configured to determine whether a hazard or fire exists by performing a fuzzy analysis of sensor readings. The fuzzy analysis includes categorizing respective sensor readings into fuzzy sets, and determining whether the hazard exists based on a combination of the categorizations. In addition the size and direction of a fire can be determined from multiple sensors.

A fuzzy logic control arrangement is provided for an impedance match network of the type that is typically employed between a source of RF power at a given impedance, e.g., 50 ohms, and a non-linear load whose impedance can vary in magnitude and phase, e.g., an RF plasma. The fuzzy logic controller fuzzifies the phase and the magnitude error signals. The error signals are applied to a fuzzy logic interference function based on a number of fuzzy sets. The values of the error signals enjoy some degree of membership in one or more fuzzy sets. Fuzzy logic rules are applied to the phase and magnitude error signals. In a defuzzification stage, drive signal values are obtained for moving the tuning elements of the variable impedances. The drive signal values are weighted according to respective fuzzy inference functions for which the error signals enjoy membership. Then the weighted drive signal values are combined to produce output drive signals.

The invention discloses a living fingerprint verifying method based on a thin-plate spline deformable model, which collects and preprocesses the fingerprint sequence of a finger for verifying, registers the fingerprints before and after deformation, selects corresponding minutiae point pair to serve as the criterion point pair for estimating the thin-plate spline model, employs improved thin-plate spline model to describe deformation of fingerprints, computes the deformable energy of the fingerprint for verifying in each direction on the basis of model parameter to an overall bending energy vector, trains respectively on a true and a false fingerprint deformation base, obtains space distribution of the bending energy vector, respectively computes similarity from the bending energy vector for measuring to an eigen fuzzy set of true and false bending energies, and selects proper threshold value to obtain the verifying result. The invention can identify false fingerprint from the living fingerprint, effectively realizes living fingerprint verification in real-time, increases safety of automatic fingerprint verifying systems, enlarges the application range of automatic fingerprint verifying systems and has important application value in the field of biological character identification.

The invention discloses an image enhancement and partition method, which comprises the following steps of I, image enhancement: a processor and an image enhancement method based on fuzzy logic are adopted for performing enhancement processing on an image needing to be processed, and the process is as follows: i, converting from an image domain to a fuzzy domain: mapping the grey value of each pixel point of the image needing to be processed to a fuzzy membership degree of a fuzzy set according to a membership function (shown in the specifications); ii, performing fuzzy enhancement processing by utilizing a fuzzy enhancement operator in the fuzzy domain; iii, converting from the fuzzy domain to the image domain; II, image partition: partitioning a digital image subjected to enhancement processing, i.e. a to-be-partition image, according to an image partition method based on a three-dimensional fuzzy division maximum entropy. The method is simple in steps, reasonable in design, convenient to realize, good in processing effect and high in practical value, and an image enhancement and partition process can be simply, conveniently and quickly completed with high quality.

Data analysis methods are characterized in terms of tangible and non-tangible parameters such as user-friendliness, accuracy, simplicity, interpretability, speed, cost, comprehensibility, and transparency, and then matched to pre-specified requirements. A data analysis method is selected in accordance with a user preference, wherein the user preference relates to a feature of the data analysis method and is represented by a fuzzy set comprising a range of values.

The invention discloses a data enhancement method for robust face identification. According to the method, a face shielding processing method is included, if an input face image has no shielding, a standard formed by all the no-shield samples is selected as a shielding mask, if the face image corresponds to one type shielding, a corresponding shielding mask is selected for reconstruction; noise is added in a manual mode, and noise enhancement processing is carried out; fuzzy set image enhancement is carried out; HsV equalization homomorphic filtering is carried out, through image spectrum analysis, influence of illumination and reflectivity on a pixel gray value is processed, the image is reduced to be a more clear one, and more details are realized; image generation is accomplished based on triangular face multi-attitude characteristics. The method is advantaged in that influence of shielding, noise, fuzziness, illumination, expression and attitude factors can be prevented in a face identification system, a problem of insufficient training data in the face identification system can be solved, and model generalization capability is improved.

The invention discloses a method of monitoring faults in sections for an intermittent control system and relates to a fault monitoring method. Firstly, a plurality of batches of collected intermittent process data are standardized in a way of expanding variables, and a data matrix on each sampling time is subjected to principal component analysis; secondly, a fuzzy C-means clustering is a fuzzy clustering analysis method which is suitable for soft partition and is generated through combining a fuzzy set theory and a k-means clustering; and thirdly, after segmentation is finished, an improved MPCA (Multiway Principal Component Analysis) model with a time varying principal element covariance on the basis of expanding variables is established on each subphase, then when on-line monitoring is carried out, which phase a new batch of data belongs to is judged, whether the data exceeds the fault monitoring control limit or not is calculated and judged, if so, a fault occurs, and the fault monitoring in sections ends. According to the invention, process multi-phase partition is more accurate, misinformation and missing report rates in monitoring are reduced, and the practical application and operability are strong.

The subject process accepts the data from a kinematic tracker and maps them to fuzzy set conditions. Then using a multitude of defined membership functions and fuzzy logic gates, generates sensor mode control rules. It does this for every track and each sensor. The Rule with the best score becomes a sensor cue, which is used to place the sensor into one of three operating modes. If there are ambiguities do to one or more vehicles coming in to close proximity to each other process compares radar profiles of vehicle to those stored in an “on the fly” data base to eliminate the ambiguities.

The invention relates to a wireless sensor network trust evaluating method based on a fuzzy logic, which comprises the following steps that: (1) an evaluating node initializes the direct trust vectors of evaluated nodes; (2) the evaluating node periodically updates the direct trust vectors of the evaluated nodes by using a fuzzy inference method; (3) the evaluating node collects the indirect trust vectors of recommended nodes on the evaluated nodes; (4) by utilizing the direct trust vectors and the indirect trust vectors of the evaluated nodes, the evaluating node calculates the comprehensivetrust vectors of the evaluated nodes; and (5) according to the comprehensive trust vectors of the evaluated nodes, the evaluating node carries out the judgement of the trust classification and the decision of the network collaboration on the evaluated nodes. The invention realizes the trust quantification by adopting the trust value definition based on a fuzzy set theory and using a fuzzy inference algorithm, thereby effectively handling the problem of the subjective fuzziness of the trust and simulating the subjective cognitive process of the trust inference. Compared with the traditional algorithm, the wireless sensor network trust evaluating method has higher sensitivity, accuracy and generality.

The invention relates to the field of pattern recognition, and discloses an unbalanced data classification method based on intuitive fuzzy integration and a system based on the method. The method comprises the following steps of: a) cleaning original data, and classifying original point-of-sale (POS) class samples according to intra-class positions to generate POS class artificial samples; b) training a base classifier by using different sample sets of inter-class approximate balance; c) converting the classification output equal utility of the base classifier into an intuitive fuzzy matrix; and d) integrating samples to be classified into the membership and the non-membership of the POS class and the negative (NEG) class by combining the weight of the base classifier, and making a classification decision. The invention has the advantages that: over learning is avoided by integrating over sampling and under sampling; the training samples of the base classifier are different, so that the difference of the base classifier is ensured; the base classifier is not specifically limited, so the method has good expandability; the intuitive fuzzy reasoning method quantitatively describes the uncertainty in classification so as to improve the performance of integrated learning; therefore, the system based on the method can better support the medical diagnosis decision and the like.

The invention relates to an overlapping community discovering method based on spectral clustering and fuzzy sets. The overlapping community discovering method comprises the steps that 1, data sets ofa social network are read to generate a network structure graph, and the attribute information of nodes in the network is acquired; 2, the Jaccard coefficient and the attribute information of the nodes in the network are combined to calculate the similarity value among the nodes in the network; 3, a similarity matrix is built based on the similarity value among the nodes, and accordingly the normalized Laplacian matrix is built; 4, the feature vector and the feature value of each node are calculated, and a new feature vector is generated by utilizing methods of iteration and compression; 5, the new feature vector is orthogonalized, the membership grade is calculated, and the nodes with a plurality of high community membership grade values are subjected to division of overlapping communities; 6, the community division meeting the highest modularity requirement is selected according to the modularity divided each time; and 7, the final community division result is output. The overlappingcommunity discovering method can efficiently and accurately discover and divide the overlapping structures in the complex network.

The invention discloses a multi-target distribution network reconstruction method considering distributed generation and load uncertainty. Wind power output force, photovoltaic power generation and load uncertainty are taken into consideration in the method. The three most important evaluation indexes, of optimized operation of a distribution network, including active loss, minimal voltage value of nodes and load balancing degree, namely three optimizing reconstruction targets, are given. Wind power output force, photovoltaic power output force and load uncertainty factors are processed through a scene analytical method. The final reconstruction scheme is obtained through multi-target disturbance biogeographic algorithm and fuzzy set theory. A multi-target reconstruction decision solution can be rapidly found, and high searching efficiency is ensured. Under an absorbing distributed power supply condition, various indexes of a network can be improved substantially through network reconstruction, so that the reconstruction is more adaptable to a real decision-making process.

The invention belongs to the technical field of information security, and discloses a searchable encryption method and system for supporting fuzzy retrieval of word morphology and word meaning in cloud environments. The method includes establishing a security index by adopting a Bloom filter structure to support file dynamic update; accomplishing word morphology fuzzy matching by adopting a locally sensitive Hash function; performing word meaning expansion on keywords by using a natural language toolbox, constructing a word meaning fuzzy set, and inserting the word meaning fuzzy set into the established security index so that support word meaning keyword retrieval can be converted into accurate matching retrieval; performing inner product operation to realize the ordering of retrieval results by establishing query vectors and index vectors; and using the Hash function and a security knn algorithm to make the method and the system effectively resist selection keyword attacks so that thesearching of cryptograph on a cloud server under semi-trusted conditions. Cryptograph searching functions can be achieved; and the method and the system can support fuzzy retrieval of word morphologyand word meaning, multi-keyword retrieval, and dynamic adding and deleting files.

The invention discloses a health management decision-making method suitable for a complex process system. The method comprises the steps of 1, acquiring a system measuring point parameter; 2, pre-processing the system measuring point parameter; 3, performing real-time fault detection on the pre-processed system measuring point parameter through an adaptive threshold analysis method, and performing fault diagnosis on the fault detection result by using a knowledge reasoning method in combination with historical data, a fault mode and an influence analysis table; 4, evaluating the health degrees of single-parameter sensors by combining the fault diagnosis result and using a grey theory method, and fusing the health degrees of the single-parameter sensors by using a fuzzy set fusion theory to obtain a fault mode health parameter; 5, predicting a fault mode health parameter by using the obtained fault mode health parameter through a relevance vector machine method; and 6, fusing maintenance decisions of multiple decision theories by using a grey group decision-making theory to obtain a maintenance decision result. The method can be used for fault prediction for next operation of the system, and provides a maintenance advice for each fault mode.

The subject process accepts the data from a kinematic tracker and maps them to fuzzy set conditions. Then using a multitude of defined membership functions and fuzzy logic gates, generates sensor mode control rules. It does this for every track and each sensor. The Rule with the best score becomes a sensor cue, which is used to place the sensor into one of three operating modes. If there are ambiguities do to one or more vehicles coming in to close proximity to each other process compares radar profiles of vehicle to those stored in an “on the fly” data base to eliminate the ambiguities.

The invention discloses a parking space detection method, a system thereof and a parking space occupied probability calculation device. The method comprises steps of: collecting multiple axial magnetic field intensity of a detection point position in a parking space; comparing each of the multiple axial magnetic field intensity with a respective background magnetic field value respectively, and obtaining each axial magnetic field intensity variation; by using a membership function, establishing mapping relations between multiple axial magnetic field intensity variations and respective axial multiple fuzzy sets respectively; merging a fuzzy set corresponding to each axial magnetic field intensity variation into a combination, and carrying out table look-up according to the combination to obtain a probability of being occupied of the parking space. According to the method, fuzzy theory is used, setting a threshold in a calculation process is not needed, and a problem that measurement accuracy is influenced by a threshold set difficulty and an unreasonable threshold is avoided. In addition, according to the method, an occupied or unoccupied state of the parking space can be judged, a state of a vehicle in getting in and out can be judged, and the precision of detection is raised.

The invention provides a power-grid industrial control protocol vulnerability mining system and method based on fuzzy test. The system includes a traffic collection module, a message format identification module, a fuzzy test module and a server monitor module; the traffic collection module, the message format identification module, the fuzzy test module and the server monitor module are connected sequentially in series. The invention provides the power-grid industrial control protocol vulnerability mining system and method based on fuzzy test, which solve a power-grid industrial control protocol vulnerability mining problem. By the use of the system and method provided by the invention, a power-grid industrial control protocol can be flexibly selected according to a power-grid industrial control special circumstance, so that efficiency and accuracy of the fuzzy test can be effectively improved.

The invention relates to an image segmentation method based on improved intuitionistic fuzzy C-means clustering, belonging to the image segmentation field. Firstly, an improved non-membership functionis proposed to generate intuitionistic fuzzy sets, and a method based on gray feature is proposed to determine the initial clustering center, which highlights the role of uncertainty in intuitionistic fuzzy sets and improves the robustness to noise. Secondly, the improved nonlinear function is used to map the data to the kernel space in order to measure the distance between the data points and the clustering centers more precisely. Then we introduce the local space-Gray level information, considering membership degree, gray level features and spatial position information. Finally, the intuitionistic fuzzy entropy in the objective function is improved, and the fuzziness and intuitionism of the intuitionistic fuzzy set are considered. The invention can effectively overcome the influence ofnoise and blur in the image on the algorithm, improves the segmentation performance of the algorithm, the pixel clustering performance and the robustness, is suitable for various different types of gray-scale images, and can obtain more accurate segmentation results.

An interactive product configuration platform comprises a two-dimensional (2D) or three-dimensional (3D) information interface module, a product case base module, a parts base module, a product and parts management module, an optimal solver module, an embedded 3D product holographic information interaction module and a parameter drive modeling module. The 2d or 3D information interface module is relevant to a general bill of material (GBOM), and used for obtaining of user demands and generating of a bill of material (BOM). The product case base module and the parts base module have online self-learning ability. The product and parts management module is based on cloud computing. The optimal solver module is relevant to product structures and a case base, and a quadratic parabola fuzzy set case-based reasoning method is adopted for the optimal solver module which is used for matching and solving of an optimal case which is in the case base and most similar to a customized product. The embedded 3D product holographic information interactive module is relevant to the case base, and used for previewing of holographic cases of optimal products by a user and interactive information modifying. The parameter drive modeling module is relevant to the optimal cases, and used for establishing of a 3D model of the customized product driven by automatic parametric characteristics according to the user demands and interactive information. The interactive product configuration platform achieves 3D interaction, and improves product development and design efficiency.

The invention provides a fuzzy test case generation method suitable for an industrial control private protocol. The method comprises the following five steps of data preprocessing, feature generationand selection, protocol data frame clustering, format extraction with a variation strategy and fuzzy test case generation. According to the method, the possible type of the field is deduced accordingto the value change characteristics of each offset position in the industrial control private protocol, and the variation strategy of the fuzzy test is further generated, so that the analysis cost ofthe industrial control private protocol is reduced, the industrial control private protocol can be subjected to targeted variation, and the generation efficiency of the test case is improved.

A passenger train operation scheme diagram drawing method disclosed by the present invention comprises the steps of constructing a railway road network topological structure diagram, establishing a road network topological structure diagram based on stations; according to the road network topological structure diagram, utilizing a fuzzy set to describe a target function convenient for passengers to travel, and establishing a road network passenger train operation scheme diagram drawing and optimization model by taking the station and path capability as the constraint; utilizing a rough-fuzzy soft set theory to describe the important degrees of the road network stations, and transforming the road network passenger train operation scheme diagram drawing and optimization model into a passenger train operation scheme diagram drawing model which is drawn according to the important degrees of the stations; according to an ant colony algorithm based on the station and path capability cooperation, solving the passenger train operation scheme diagram drawing model which is drawn according to the important degrees of the stations rapidly to obtain a passenger train operation scheme diagram. By the technical scheme of the present invention, the implementation possibility of the passenger train operation scheme diagram is conducive to being improved.

The invention relates to a reliability analysis method for an electromechanical actuator based on a fuzzy dynamic fault tree. According to the method, top, intermediate and bottom events of the electromechanical actuator analyzed and determined to establish the dynamic fault tree, a dynamic logic gate is replaced with an algebraic operator, an algebraic model of the dynamic fault tree is converted into and solved, data of a triangular fuzzy set is used to represent the fuzzy reliability of the bottom event, the algebraic model is decomposed into a static subtree and a dynamic subtree for solution, the fuzzy probability of failure of an electromechanical actuator system is obtained via integration, and a weak link of the electromechanical actuator system is determined by analyzing the fuzzy importance degree. The method of the invention can be used to analyze the reliability of the electromechanical actuator effectively, solve the problem that traditional dynamic fault tree analysis is high in the computational complexity, and provides reference for fault positioning of the electromechanical actuator.

The invention discloses a highway tunnel health status dynamic evaluation method based on the variable fuzzy set theory. The method comprises the steps of constructing a highway tunnel health status evaluation index system with lining fissures, lining strength, lining thickness, corrosion of reinforcement, lining deformation, back cavities, pavement distress, harmful gas and lighting distress as evaluation indexes as well as the qualitative and quantitative criterion of the evaluation grade and each evaluation index of highway health status; and through repeated and regular detection of the highway tunnel evaluation indexes by a tunnel diagnosis and fast repairing integrated car and based on the variable fuzzy set theory, determining highway tunnel detection node health status values and the health status deteriorating speed mean value and establishing a highway tunnel health status dynamic evaluation model by means of repeated detection data of the highway tunnel health status evaluation indexes and the criterion of the evaluation grade and each evaluation index of the highway health status. The highway tunnel health status dynamic evaluation method based on the variable fuzzy set theory not only achieves objective evaluation of the highway tunnel health status, but also can be used for dynamic prediction of the highway tunnel health status.