81 results about "Recursion" patented technology

The invention discloses a box-type substation state monitoring and early warning method based on artificial intelligence, relates to the technical field of intelligent power grids, and aims to solve the problems of missing report, false report and response lag caused by the fact that an existing static threshold ignores multi-physical coupling and a depth model highly depends on scarce fault samples. According to the scheme, sliding window kernel density estimation is carried out on a multi-channel time sequence signal, a dynamic coupling matrix is constructed through recursion Copula decomposition, a three-level threshold surface is generated through time-varying quantile regression, abnormal samples and graph attention network extraction state representation are generated in combination with a conditional variation auto-encoder, lightweight recursion pruning is carried out, and the dynamic coupling matrix is obtained. An abnormal score is generated through a multilayer Bayesian network and particle filtering, a multi-step risk trend is discriminated through a Gaussian kernel derivative slope, and finally unscented Kalman filtering is used for smoothing and online threshold correction; according to the method, the detection sensitivity and the early warning recall rate of the box-type substation to the transient coupling fault are remarkably improved, the response speed is improved, and the false alarm frequency is effectively reduced.

The invention relates to the field of augmented reality, and provides a display system and method based on the augmented reality technology, and the method comprises the steps: firstly obtaining a virtual scene image and a real scene image, carrying out the semantic-level feature fusion of the scene images, synchronously obtaining the natural language description of the interaction intention of a user, and carrying out the semantic understanding; then, semantic-level feature coding representation of virtual and real scenes is extracted, multi-granularity semantic embedding of natural language description is combined, user intention representation is enhanced layer by layer by means of a recursive cross-modal attention mechanism, and finally an interaction picture highly matched with the context of the scene is generated. Thus, through a cross-modal hierarchical interaction recursion mechanism, the limitation of traditional single-modal intention recognition is broken through, deep coupling of dynamic scene semantics and user language intentions is realized, a new technical path is provided for high-immersion AR interaction, and the sense of participation of the user is improved.

The invention belongs to the technical field of energy system planning, and particularly relates to a park integrated energy system stochastic planning method and system based on multiple uncertainties, and the planning method comprises the steps: building a probability model of a multi-energy load growth rate based on park industrial planning and historical data; utilizing Monte Carlo simulation and K-means clustering to generate a representative load scene tree; establishing an upper and lower boundary prediction model of the energy price and the equipment cost by adopting a quantile regression forest method; constructing a multi-stage collaborative optimization model taking the minimum comprehensive cost expectation as a target, and considering constraint conditions such as power flow, operation, time sequence and space; and carrying out reverse recursion solution by utilizing a dynamic programming algorithm, and outputting an optimal equipment configuration and construction scheme of each stage. According to the method, the problems of load increase unpredictability and energy market price fluctuation risk in different development stages of the park energy system are solved by combining scene analysis, data-driven modeling and a dynamic optimization mechanism.

The invention discloses an automatic compliance examination method and system based on graph retrieval enhancement. The method comprises the following steps of: obtaining a billing specification and a historical inquiry report as input documents; the input document is subjected to dual-mode document segmentation based on a large language model, logic blocks and physical blocks are generated, and each logic block is a continuous page unit and is attached with a content abstract generated by the model; constructing a precedent graph, extracting key legal elements in the historical inquiry report through multi-stage recursion, generating semantic network nodes carrying traceability identifiers, and establishing cross-document semantic association; and constructing a state graph, automatically identifying chapters and logic and semantic relationships among the chapters based on a document hierarchical structure, and generating a machine-readable structured index and the like. According to the method, the long text semantic understanding depth, the cross-section consistency and the legal reasoning accuracy are remarkably improved, the manual review cost is reduced, and the method is suitable for a listing compliance review scene under a registration system.

The invention relates to the technical field of data processing, in particular to a multi-modal homework intelligent correction feedback method and system. Through multi-modal alignment of texts, images and audios, potential errors and difference characteristics are highlighted according to a difference perception attention mechanism, and the sensitivity to local errors is improved; in combination with depth feature representation of cross-modal Transformer, homework overall scoring and element-level error positioning are completed, a semantic mapping matrix of questions and knowledge points is constructed, score weight is introduced, threshold filtering and historical mastery recursion are performed, and in combination with a forgetting attenuation factor, change of a learning state is dynamically reflected, so that accurate intelligent correction and personalized feedback recommendation are realized.

The invention discloses a threat entity recognition and intelligence processing method based on BERT assistance, and the method comprises the following steps: S1, collecting and preprocessing threat intelligence text data, and constructing an input corpus data set; s2, inputting to a BERT encoder, extracting context semantic vectors, and obtaining a semantic feature vector sequence; s3, inputting the semantic feature vector sequence into a gating recursion unit, modeling time sequence features, and outputting a hidden state sequence; s4, based on the hidden state sequence, performing threat entity label labeling through a decoding layer; s5, adopting a dung beetle optimization algorithm to optimize BERT and gating recursive unit parameters; and S6, performing reasoning prediction by using the optimal parameter, and outputting and storing structured threat entity information. According to the method, the BERT encoder, the gating recursion unit and the improved dung beetle optimization algorithm are fused, so that high-precision recognition and structured extraction of the threat entities in the threat intelligence text are realized.

The invention relates to an elastic control method of a high-order nonlinear system under pulse false information injection attack. The method comprises the following steps: acquiring a system output signal and a control input signal of a current control period, and a state estimation value vector and a fuzzy weight vector estimation value of a previous control period; inputting a system output signal and a control input signal of a current control period, and a state estimation value vector and a fuzzy weight vector estimation value of a previous control period into a nonlinear state observer based on a fuzzy logic system to obtain a state estimation value vector of the current control period; the controller based on the fuzzy logic system carries out backstepping recursion through the state estimation value vector of the current control period and the fuzzy weight vector estimation value of the previous control period to obtain an elastic control input signal; and obtaining a fuzzy weight vector estimator of the current control period through an adaptive law based on the state estimation value vector of the current control period. By adopting the method, state jump and performance damage of attacks can be resisted.

The invention discloses a retrieval report generation method and device based on a local language model, equipment and a medium, and relates to the technical field of information retrieval, and the method comprises the following steps: receiving query configuration information and a query statement; using a local language model to determine an enhanced query based on chain thinking; splitting the enhanced query, and calculating a correlation score between the sub-query and the enhanced query based on text embedding so as to perform sub-query screening; on the basis of query configuration information, a preset recursion strategy and a dynamically adjusted correlation threshold value, correlation-guided recursion webpage retrieval is conducted on the screened or selected sub-queries; constructing a target knowledge base based on the recursive retrieval result and a local knowledge base supporting multiple modes; and generating a target retrieval report based on the local language model, the target knowledge base, the enhanced query and the target recursive retrieval result. According to the method, the quality, the accuracy, the comprehensiveness and the relevance of the retrieval report are improved by combining relevance-guided adaptive recursive exploration and multi-modal knowledge fusion.

The invention discloses a BIM-based large-space pipeline installation method and system, and relates to pipeline installation: performing three-dimensional gridding processing on BIM model data, generating a gridding space model, and constructing a pipeline installation space; calculating the load per unit length of the pipeline according to the self weight of the pipeline and the weight of a medium in the pipeline in the physical parameters; a dynamic planning method is adopted for optimization arrangement of the supports and hangers; constructing a state transition equation according to the load per unit length of the pipeline and the structural mechanics continuous beam model; according to the state transition equation, an optimal decision sequence is obtained through reverse recursion solution, backtracking is conducted from the tail end of the pipeline to the starting point, in each decision stage, the position of a supporting point enabling accumulated deflection of all follow-up pipe sections to be minimum is selected, and a globally optimal supporting and hanging bracket arrangement sequence is generated; according to the globally optimal arrangement sequence of the supports and the hangers and the load per unit length of the pipeline, the deflection value of each pipe section is calculated; according to the method, the problem of non-uniform deflection distribution caused by a traditional local optimization method is avoided.

The invention discloses a vulnerability assessment and reinforcement method based on multi-agent cooperative reasoning and related equipment. The method comprises the following steps: a, obtaining reasoning data including reasoning facts and rules; b, taking the penetration test host as an initial node, taking a test target as a termination node, and generating a potential attack path through an attack path reasoning agent; c, removing repeated paths by using an attack path deduplication agent; d, performing prefix sub-path recursive reasoning on the residual paths by the sub-path exploration agent to generate extended attack sub-paths; e, removing repeated paths in the extended sub-paths through a sub-path duplicate removal agent; f, replacing the initial node with the reasoning initial node of the remaining extended sub-paths, recursively executing the steps b to f, exiting the current recursion stage when no new extended sub-path is added, and terminating recursion until no new potential attack path is added, so as to obtain a final attack path set; and g, recording vulnerability utilization frequency in the set, and selecting vulnerability reinforcement according to the vulnerability utilization frequency. According to the method, the problems of insufficient understanding of massive network configuration information, attack path combination explosion and repair decision optimization under service constraints can be solved, and dynamic deduction and active defense of network attack paths are realized.

The invention relates to the technical field of natural language processing, and provides a document segmentation method based on context marking and model cascading, which comprises the following steps: in response to a document segmentation request, loading a to-be-processed document and initializing segmentation parameters; calling a large language model to analyze a current to-be-processed text segment, identifying a logic demarcation point, and generating wedge information containing a segmentation point mark and a context; positioning absolute positions of segmentation points in the text segment according to the wedge information, and segmenting the text segment into a plurality of text sub-segments; repeating the execution until a preset recursion termination condition is reached; after recursion is completed, segmenting results of all layers are aggregated, a hierarchical document structure is constructed, and the segmenting results are output. Through a wedge mechanism, only tiny positioning marks are output, the token cost is reduced, and the overall cost is optimized in combination with a model cascading strategy. And the generated text block is highly aligned with the semantic boundary of the document, so that the context fragmentation problem is effectively solved, the context relevance is enhanced, and the model illusion is inhibited.

The invention discloses an index system early warning method and device, equipment, a storage medium and a program product, and relates to the technical field of operation and maintaining.The method comprises the steps that a logic expression input by a user in a similar natural language form is analyzed, an abstract syntax tree is generated, recursion processing is conducted through a recursion decline interpreter, and an executable rule object is obtained; scheduling the executable rule object according to a preset priority weight to obtain a total scheduling result, the preset priority weight being a weight allocated for the priority of each early warning rule; and when the early warning rule is triggered, carrying out early warning on the abnormal indexes in the logic expression. According to the method, the logic expression is introduced to dynamically define the index, then the logic expression is analyzed into the abstract syntax tree, and dynamic compiling of the abstract syntax tree is realized by using the recursive decline interpreter, so that the flexibility of index early warning can be improved through the introduced logic expression in a complex monitoring scene, and the early warning efficiency is improved. Therefore, the efficiency of index operation and maintenance response is improved.

The embodiment of the invention provides a method for carrying out calculation based on a data flow graph (DFG) and related equipment. The method comprises the steps that a first operator obtains a plurality of data values from a second operator, the data flow diagram comprises the first operator and the second operator, and the data values are stored in a plurality of instruction units corresponding to the second operator in the DFG respectively; the first operator calculates an output result of the first operator according to the plurality of data values in the second operator, and the plurality of data values in the second operator are input of the first operator. By the proposed techniques, cycling or recursion may be performed in the DFG.

The application discloses a kind of communication power supply operating state intelligent monitoring methods based on machine learning, comprising the following steps: S1, obtains multiple-source monitoring data and pre-processes;S2, sequence segmentation is carried out using sliding time window and statistical feature vector is extracted, forms state variable set;S3, each state variable is encoded to construct state node and establish weighted directed connection, and construct operating state evolution diagram;S4, state transition matrix is constructed, main transition feature is extracted and low rank is approximately reconstructed, sequence learning is carried out to construct state update function;S5, to state node, multiple-step recursion is carried out, and the Euclidean distance between recursive state and risk boundary is calculated, to determine state monitoring result;S6, error is calculated and state update function parameter is iteratively updated.The application can realize the multiple-step recursion prediction and risk trend identification of communication power supply operating state, improve the accuracy and stability of communication power supply operating state monitoring.

The application discloses a quasi-cyclic low-density parity-check code decoding method, system, device and medium, acquires a predetermined parity-check matrix; adjusts the parity-check matrix into a target operation matrix composed of a unit matrix and a displacement matrix, the dimensions of the unit matrix and the displacement matrix are both p x p; acquires a target codeword to be decoded corresponding to a current decoding recursion number; splits the target codeword into target sub-codewords with a length of p; in the target operation matrix, determines a target operation sub-matrix corresponding to each target sub-codeword; performs parallel operation on the target sub-codewords and the target operation sub-matrix to obtain a syndrome value of the target codeword; determines a current decoding result of the current decoding recursion number based on the syndrome value, and determines a target decoding result based on the current decoding result. In the application, parallel operation is performed on the corresponding target operation sub-matrix and the target sub-codeword, the operation efficiency of the syndrome value can be improved, and the operation efficiency of the entire decoding method is further improved.

Disclosed in the present invention is a real-time recursive CRC method. The method comprises: sequentially extracting from a real-time bit stream a1a2a3… binary sequences S1S2…Si… at non-fixed lengths; bisecting into left and right parts each binary sequence segment Si, so as to obtain sub-sequences, and repeatedly bisecting into left and right parts the sub-sequences until the lengths of finally obtained sub-sequences are less than a preset value; and calculating a CRC check value and check matrix of each final sub-sequence, and performing level-by-level upward merging calculation on left and right sub-sequences, so as to obtain a final check value of the entire real-time bit stream. Further disclosed in the present invention are a corresponding CRC apparatus and a computer storage medium and a program for implementing the CRC method. In the present invention, a method combining iteration with recursion is used to calculate, in real time, a check value of a variable-length binary sequence stream, thereby achieving logarithmic calculation time complexity and meeting CRC operation requirements of a real-time ultra-high-speed data stream.

The invention discloses a logical operation method based on depth-first traversal and stack recursion, and belongs to the technical field of computer logical operation and data processing. The method aims at solving the problems that in the prior art, when a complex nested logic expression is processed, operator priority judgment is complex, short circuit evaluation is difficult to achieve, and code maintainability is poor. The core of the method is that firstly, a nested logic expression to be calculated is abstracted into a tree-shaped or nested array data structure; secondly, a depth-first traversal algorithm is adopted to conduct recursive scanning on the data structure, and in the traversal process, encountered logic operators and basic judgment conditions (calculation factors) or intermediate calculation results of sub-expressions are sequentially pressed into a main stack according to the traversal sequence; when traversal of one sub-expression is completed, an actuator module is automatically triggered; and the executor module pops up a logic operator of the current action range from the main stack.

The invention provides a multilayer solving method for accelerating circuit simulation, electronic equipment and a storage medium, and the method comprises the steps: carrying out the recursion division of a circuit into sub-circuits of a plurality of hierarchies, and forming a hierarchical structure from fine to coarse; on the basis of circuit delay attributes, marking and simplifying inactive parts in each level of sub-circuit, and constructing a mapping relation between levels at the same time; based on the hierarchical structure and the mapping relation, assembling a to-be-solved linear equation set; a multi-layer class method is adopted to solve the linear equation set, and the multi-layer class method comprises the steps that smoothing is carried out on a fine layer before iteration, a residual error is transmitted to a coarse layer, a direct method is adopted to solve the coarsest layer, and then a solving result is extended back to the fine layer and smoothing is carried out after iteration; and repeating the solving step of the multi-layer class method until the linear equation set converges. The system of linear equations is established based on a multi-level circuit structure and is solved by using a multi-level method, so that the solving time and memory occupation of the system of linear equations in circuit simulation are greatly reduced.

The application provides a high-order relationship modeling method between topological nodes for topological behavior recognition, designs an iterative modeling method from low-order relationship to high-order relationship based on recursion, and realizes the completeness of modeling through the recursion self-similarity and scalability. The application models the high-order relationship of the completeness through generating the complete dependence between the low-order relationships, extracts more extensive and abstract individual features, effectively utilizes the self-similarity and iteration of the recursion, obtains the modeling complexity of the high-order relationship which is linearly related to the order, thereby avoids the factorial order complexity caused by the introduction of the completeness hyperedge, obtains the advanced features of the individuals in the group containing the high-order information, optimizes the modeling process of the graph neural network, and is beneficial to the group behavior recognition task.

The invention relates to the technical field of data processing and prediction, and discloses a space-time sequence prediction method and system and computer equipment, a bottom layer steadily extracts space local and short and medium-term dynamic states through a convolution gating loop unit, an upper layer captures cross-site and cross-scale long-range dependence through a space-time self-attention Transform, and the space-time sequence prediction method comprises the steps that the space local and short and medium-term dynamic states are obtained; and a multi-step result is stably output at a decoding end, and position coding and attention visualization are provided at the same time to enhance the interpretability and maintainability of the service. Through recursion of input in a time dimension by using a convolution gating cycle unit, a space structure is maintained, space information loss caused by traditional RNN straightening is avoided, time position codes and space position codes are superposed, space-time position codes and space constraint attention are provided as selectable features, and physical consistency and interpretability are improved.

The invention provides a charging load prediction method and device based on dynamic game and spatio-temporal state evolution, and the method comprises the steps: building a simultaneous time recursion equation under a discrete spatio-temporal framework based on a multi-source impact factor parameter; the simultaneous time recursion equation is used for describing the joint evolution process of at least two different types of driver states under a discrete space-time framework, the influence of external factors on individual states is reflected, the game logic in a group is also reflected, the prediction result conforms to the real driver behavior logic, and the prediction accuracy is improved. A joint recursion relation is determined and obtained on the basis of the time recursion equation through iterative calculation on a discrete time axis until the joint recursion relation reaches a stable state, a curve of the total charging power of the target charging station changing along with time is extracted from the stable state, and a charging load prediction result is obtained. It is ensured that the prediction result is stable output after long-term evolution of the system and fits the load law in actual operation of the charging station.

The invention provides a target data association method based on reinforcement learning, and belongs to the field of information fusion and data processing. Firstly, on the basis of the criterion that one target only generates one trace point and one trace point is only related to one target, all data of adjacent seven sampling intervals are traversed, and new target detection is carried out. Then, according to the speed threshold, an annular gate screening mechanism is set; then, the association probability between the target and the trace point is obtained through the long-short-term memory network, and then the trace point is selected; and calculating the reward value of the selected trace point in combination with a Bayesian network and a Bayesian recursion function. Finally, an adjustment mechanism capable of automatically correcting trace point association errors is designed, and the problem of weak reinforcement learning migration ability is solved. The method has the advantages of being wide in application range, high in robustness, high in application value and the like, and the proposed data association method can be directly used for solving corresponding practical problems.

A total least squares recursive Lagrangian estimator for estimating parameters of a physical system (100) considered to be linear, such as a motor vehicle. An estimation device (110) comprises a measurement unit (114) configured to obtain successive input measurements Ak and successive output measurements yk of the linear system, and an estimation unit (116) configured to estimate one or more parameters xk recursively from the input and output measurements. A k+1 estimation recursion comprises the following operations: (1) (2) (3) (4) (5) optionally (5') (6) (7) where Qa,k and Qy,k are weighting matrices; δxk, and Pk are inter-recursion variables; is an instrumental variable correlated with the variables; Qz,k, ΔAk, Kk are internal variables; α is a regularization term and λ is a forgetting parameter. Figure for abstract: Fig 2

The present application relates to the technical field of computer, disclose a kind of saxophone sound physical modeling computer simulation method.The method comprises: according to saxophone geometric parameter and fingering configuration, its acoustic cavity is divided into multiple acoustic sub-sections;The geometric characteristics of each sub-section are modeled using one-dimensional non-uniform digital waveguide, two-dimensional axisymmetric finite difference time domain or three-dimensional boundary element local resonance unit respectively;Interface coupling model is constructed to meet the continuity of sound pressure and the conservation of volume velocity;Combined with the reed excitation model based on the nonlinear Bernoulli equation, the sound field state of each unit is calculated synchronously by multi-rate time domain recursion algorithm, and high-fidelity audio data stream is output.The present application improves the simulation efficiency significantly while ensuring the timbre fidelity through heterogeneous modeling and multi-rate strategy, and realizes the collaborative optimization of precision and real-time.

This invention relates to a pilot decision-making deduction method based on dynamic optimization of a multi-level fuzzy branch structure, comprising the following steps: 1) quantifying the set of decision states; 2) quantifying the set of fuzzy rules; 3) designing multiple key influencing characteristics such as hysteresis factors, pilot skill level stability factors, and search factors to establish a multi-level fuzzy branch structure relationship that reflects the pilot's decision-making logic, and using it as the pilot decision-making deduction mechanism in the human-machine system; 4) dynamically optimizing the multi-level decision logic and the decision-making deduction sequence results during the time-series recursion process. Compared with existing technologies, this invention introduces a logical thinking mode of human cognition mechanisms and decision-making characteristics by designing multiple decision levels and their rule changes, overcoming the shortcomings of traditional pilot models such as fixedness, static parameters, and poor transferability, while also possessing the advantages of adaptability to various aircraft types and wide application range.

The invention discloses a preset time adaptive control method and system for a high-order nonlinear MIMO system, and relates to the technical field of industrial control. The method comprises the following steps: establishing a dynamic model of a high-order nonlinear MIMO system, and approaching an unknown nonlinear function in the dynamic model by using a neural network; determining a tracking error based on the dynamic model; constructing a virtual control law based on the tracking error and defining a virtual error; constructing a preset time filter to process the virtual control law, and determining a filtering error; the parameter of the preset time filter is configured to enable the filtering error to converge in the preset time; under a backstepping recursion framework, combining a tracking error, a virtual error and a filtering error, and adopting an improved weighted integral technology to design an actual control law; and setting an event triggering mechanism based on a relative threshold value to modulate the actual control law and then output a control signal. The system stabilization time can be directly preset without being influenced by the initial state, and meanwhile the updating frequency of the controller is reduced.

The embodiment of the invention provides a recursive query processing method and a related device, and relates to the technical field of big data, the method comprises the following steps: when a statement to be processed is determined to be a recursive query statement, extracting a table name and a recursive condition in the recursive query statement; according to the table name and the recursion condition, constructing a directed graph, and based on the directed graph, obtaining the maximum depth of nodes and the total number of paths; splitting the directed graph into a plurality of sub-graphs according to the maximum depth and the total number of paths; and determining a link from a starting node to a target node according to the plurality of sub-graphs, and outputting the link. According to the method, the recursive query table data is abstracted into the directed graph, so that the success rate and efficiency of recursive query can be improved.

The application discloses a GPU-based ARX type block cipher optimal differential characteristic search method, which first selects a high-probability differential transition to split the whole search process into forward search and reverse search; differential transition construction and probability calculation are taken as kernel functions, a GPU-based parallel scheme is designed, and the differential transition probability of different states in each round is calculated in parallel; the GPU parallel calculation result and the set probability threshold are taken as the determination condition of pruning in the search process, the optimal differential characteristic is searched round by round according to the branch and bound strategy, the forward and reverse searches are completed, and the optimal differential characteristics of the two parts are obtained; finally, the optimal differential characteristics of the two parts are spliced to obtain the optimal differential characteristics of the complete rounds. The search method can effectively reduce the recursion depth, improve the search efficiency through parallel calculation, shorten the calculation time and improve the search speed of the ARX type block cipher optimal differential characteristic.

The invention relates to a binary finite field polynomial multiplication acceleration method based on a GPU, and the method comprises the following steps: 1, vector sampling, 2, polynomial division: firstly dividing a polynomial into three parts, and in order to guarantee equal division as much as possible, enabling the length of each part to account for one third of the original length, and 3, high-order multiplication, after the two polynomials are respectively divided, a three-division Karatsuba method, a fourth step of multiplication recursion, a fifth step of a low-factorial multiplication method, a sixth step of result merging and a seventh step of parallel processing are used. According to the technical scheme, the problem that polynomial multiplication performance on a traditional binary finite field is poor is solved, and the multiplication structure of polynomial multiplication is improved, so that the multiplication times of polynomial multiplication are reduced, and multiplication time consumption is reduced.

The invention relates to the technical field of computers, and provides a correctness verification method and device for a large-scale text sequence BWT, equipment and a medium. The method comprises the following steps of: blocking a character string X and a suffix number group of the character string X, and shrinking the character string X by recursively calling block induction sorting until a character in the shrunk character string X1 is unique; in the backtracking stage of the recursive 0 layer, calculating the sequence of S * type suffixes of X according to the suffix array of X1, and calculating the hash value of a descending S * type suffix sequence; according to the sequence of the S * type suffixes of the character string X, calculating L type and S type suffix preamble character sequences of each block and hash values of the L type and S type suffix preamble character sequences; calculating an L * type suffix sequence and the hash value of each block of the L type BWT sequence by adopting a small top heap; calculating the hash value of each block of the S * type suffix sequence and the S type BWT sequence by adopting a large top heap; and combining the two types of BWT sequences, and outputting a final verification result, so that the space-time overhead of BWT correctness verification is reduced.