175results about How to "Efficient search" patented technology

The invention relates to the technical field of knowledge maps, and discloses a knowledge map searching method and system based on local semantics and global communitization, and the method comprises the steps: extracting entities and relationships thereof from a knowledge map to construct an undirected graph; the undirected graph is divided into multiple levels of community information, and a community report is obtained by generating a community information table; selecting an instance triple containing data source information from the knowledge graph, constructing an instance triple information source table, extracting IDs, names and attributes of entities, and generating an entity attribute table to obtain an entity attribute vector table; key entities in a to-be-queried question are extracted and subjected to vector representation, the similarity between the key entities and an entity attribute vector table is calculated, first N related entities are selected to generate contexts (including an entity context, a relation context, a data source context and a community context), and finally a search result of the to-be-queried question is obtained. According to the method, the information related to the to-be-queried question can be accurately retrieved from the knowledge graph.

The invention provides a subway pedestrian anomaly detection method and system based on an improved yov8 network and a clip model, and relates to the technical field of subway pedestrian detection, and the method comprises the steps: obtaining a video frame in a subway scene, and carrying out the preprocessing of the video frame; inputting the preprocessed frame data into an improved yolov8 neural network model to obtain an abnormal behavior target detection frame, and performing target candidate tracking on the target detection frame by using a target tracking algorithm to obtain a candidate image of a target area of a specific behavior; the method comprises the following steps: inputting a candidate image containing a target area into a clip model, inputting a set detection text into the clip model, mapping the detection text and the candidate image of the target area, performing text-image similarity matching, and optimizing a similarity matching threshold by adopting a Q-Learning reinforcement learning immune algorithm to obtain a similarity matching result; and the abnormal behavior detection accuracy is improved.

The invention discloses a liquid fertilizer application effect analysis method based on self-supervised learning. The method comprises the following steps: S1, generating initial feature data; s2, environment feature representation is generated based on a heterogeneous environment self-adaption module; s3, extracting short-term and long-term spatio-temporal characteristics of the liquid fertilizer application effect based on the dual spatio-temporal characteristic decoupling and dynamic fusion model, and generating comprehensive spatio-temporal characteristic representation; s4, generating fertilization characteristics in different situations by utilizing self-supervised learning, and eliminating environmental interference factors through a causal reasoning module; s5, generating a fertilization intention feature, and dynamically adjusting a fertilization strategy; s6, when the difference between the prediction result and the actual effect exceeds a threshold value, triggering a re-learning module to update model parameters; and S7, carrying out integrated optimization, realizing feature sharing and task coordination through multi-task collaborative learning, and carrying out automatic tuning by utilizing Bayesian optimization. The intelligent analysis of the liquid fertilizer application effect is realized through the self-supervised learning and multi-task collaborative optimization method.

The invention relates to a permanent magnet synchronous motor design method based on a multi-objective optimization algorithm, and the method achieves the efficient design and optimization of a permanent magnet synchronous motor through the combination of finite element simulation, multi-physical field coupling analysis, neural network performance prediction, and a hybrid optimization algorithm of a genetic algorithm and particle swarm optimization. The method comprises the following steps: determining a plurality of design targets according to an application scene of a motor, selecting key design variables, performing data cleaning and normalization processing, performing electromagnetic field, thermal field and mechanical field simulation by using a multi-physics field coupled finite element model, predicting the performance of the motor by combining LSTM and Transform neural network models, expanding a training data set through a data enhancement technology, and performing data enhancement on the motor. Finally, a hybrid optimization algorithm combining a genetic algorithm and particle swarm optimization is adopted to carry out global optimization and adaptive adjustment of design parameters, and a Pareto optimal leading edge is utilized to find out an equilibrium solution between design targets, so that the accuracy and efficiency of motor design are improved, and the method is suitable for various motor design scenes.

The invention discloses an intelligent optimization system and method for logistics transportation cost management, and relates to the technical field of intelligent logistics management, and the system comprises a data collection and analysis module which is responsible for collecting basic data of goods and analyzing the data; the constraint condition and transportation characteristic analysis module analyzes the characteristics of various constraint conditions and actual transportation environments in the cargo transportation process; the initial loading scheme generation module is responsible for generating an initial loading scheme according to characteristics and constraint conditions of goods; when an initial loading scheme is generated, three heuristic methods of first adaptation, optimal adaptation and worst adaptation are adopted, historical data are combined to guide generation of the scheme, and meanwhile, a genetic algorithm and a simulated annealing algorithm are introduced to further optimize the scheme, so that an approximate optimal solution is efficiently searched in a solution space.

The invention discloses a micro-grid typical scene generation method and system, and the method comprises the steps: forming a plurality of load-photovoltaic output two-dimensional time sequences based on the historical load data and historical photovoltaic output data of a micro-grid, considering multiple types of objects, considering the complex mutual influence relation between photovoltaic output and load, and generating a typical scene of the micro-grid. And the diversity and accuracy of the generated scene are fully improved. According to the method, coding and dimensionality reduction are carried out on the load-photovoltaic two-dimensional output time sequence, two-dimensional deep-level features are extracted, and complex features of load-photovoltaic time sequence data and correlation between the two are fully considered, so that the generated scene is closer to the actual situation, and the accuracy and reliability of the scene are improved.

The invention discloses a metallographic specimen grinding and polishing PID control method based on improved multi-material compatibility, and belongs to the technical field of PID control, and the method specifically comprises the steps: S10, building a multi-material compatible metallographic specimen grinding and polishing control model based on a PID controller; s20, improving an escape optimization algorithm, including initializing a population based on a satellite surge piloting mechanism, introducing an adaptive flexible factor, dynamically adjusting a panic index, updating a population position by using an elite gravity guiding method, and improving diversity and stability of elite pool updating by using a trace heuristic optimization method; s30, performing iteration by using an improved escape optimization algorithm to obtain an optimal PID controller parameter; s40, simulating a PID (Proportion Integration Differentiation) controller of the multi-material compatible metallographic specimen grinding and polishing control model; the improved algorithm not only improves the optimization efficiency, the response speed and the PID control precision, but also significantly enhances the robustness of the system and the adaptive capacity under different material conditions.

The invention relates to the technical field of source network load storage multi-objective optimization scheduling, in particular to a source load storage scheduling method and device considering energy storage life, electronic equipment and a storage medium. A particle swarm optimization algorithm adopting sine adaptive inertia weight is provided, scheduling plans of all units can be arranged, preference solutions approaching optimal values of all targets can be found more efficiently, various resources in the power distribution network can be utilized more fully, and the optimization effect is the best. According to the method, the optimal scheduling model of the active power distribution network is comprehensively established, indexes such as maintenance resource consumption of the power distribution network, energy storage life loss, scheduling resource consumption of a load aggregator and network loss of the power distribution network are analyzed, energy storage resource calling resource consumption can be greatly reduced, the service life of the system is prolonged, and the system performance is improved. And the optimization scheduling efficiency of the power distribution network is effectively improved.

The invention discloses a video cross-modal retrieval method, device and equipment and a computer medium, and relates to the field of video retrieval. The method comprises the following steps: performing cue word enhancement on a user query text through a pre-training embedding model to obtain a retrieval text; performing cross-modal retrieval on the retrieval text through the large audio and video model to obtain candidate video clips corresponding to the retrieval text; performing object recognition on the candidate video clips according to the retrieval text through an object recognition depth model to obtain a to-be-replaced label and a replacement label; generating a mask according to the to-be-replaced label through a mask decoder; and adjusting the candidate video clip according to the mask and the replacement label through the text video model to obtain a target video clip. Therefore, the user query intention can be understood by utilizing prompt word enhancement, the to-be-replaced part related to the user query prompt word part is extracted from the existing candidate video clips, and the new video generated after replacement is taken as the target video clip expected by the user.

The invention discloses a medicine bottle defect detection method and system based on machine vision and deep learning, belongs to the technical field of defect detection, and solves the problems that a twin defect classification network is sensitive to a highlight area caused by light reflection in appearance image information and the defect detection accuracy of the twin defect classification network is interfered in the existing method. The method comprises the following steps: constructing a defect detection model based on deep learning in combination with a YOLOV5 model, carrying out conjoint analysis detection on a time sequence image set by the defect detection model in combination with a medicine bottle simulation model, carrying out defect identification on the time sequence image set by the defect detection model, positioning a medicine bottle defect area, and carrying out defect detection on the medicine bottle. Classifying and marking defect types of the defect areas of the medicine bottles in a short circuit and random vector machine combined mode; according to the invention, the mean filter is introduced into the defect detection model, so that background filtering can be carried out on the image acquired under machine vision under the condition of light reflection of the medicine bottle, effective information of a small-size target image is effectively reserved, and the defect classification and recognition accuracy is ensured.

The invention discloses a tumor postoperative treatment staged planning method based on artificial intelligence. The method comprises the following steps: S1, constructing a multi-modal medical data set; s2, performing standardization processing on the acquired multi-modal medical data set to form a standardized data set which can be used for feature extraction; s3, generating a unified patient feature vector; s4, according to the characteristics of tumor postoperative treatment, dividing the whole treatment process into a postoperative immediate recovery stage, a postoperative recovery middle stage and a long-term follow-up visit stage, and defining a corresponding optimization target for each stage; s5, based on the patient feature vector and the optimization target of each stage, generating an initial treatment plan by using a deep ant colony algorithm; and S6, outputting a staged treatment scheme. According to the method, the professional and technical thresholds required by traditional modeling are reduced, and the universality and convenience of three-dimensional modeling are improved.

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things intelligent linkage control method and an Internet of Things intelligent linkage control device, and the method comprises the steps: S1, obtaining the working state of home equipment and the real-time data of environment information, and transmitting the real-time data to a central control platform; s2, preprocessing the real-time data to obtain preprocessed data; s3, analyzing the preprocessed data, and generating a corresponding linkage control instruction; s4, encoding the linkage control instruction to generate encoded data, and performing lossless compression on the encoded data by adopting a ZSTD compression algorithm to obtain a compressed code; s5, searching an optimal sending communication network by adopting a flora optimization algorithm, and sending the compressed code to the home equipment; and S6, the home equipment decompresses the compressed code by using an LZMA compression algorithm, and executes the linkage control instruction. According to the invention, the ZSTD compression algorithm and the flora optimization algorithm are adopted, the delay of data transmission and processing is significantly reduced, the overall response speed of the smart home system is improved, and the user experience is better.

The invention discloses a foot tub parameter intelligent adjustment method and system based on machine learning. The method comprises the steps of data collection, data screening, foot tub parameter prediction model establishment, global parameter optimization and foot tub parameter adjustment. The invention relates to the technical field of smart homes, in particular to a foot tub parameter intelligent adjusting method and system based on machine learning, according to the scheme, data screening is carried out by dividing data neighborhoods and defining data complexity and local density, and the accuracy and reliability of abnormal value recognition are improved; a model is established by designing an excitation function, a front double transformation layer, a residual adjustment layer and a loss function, so that the expression ability, prediction accuracy and prediction error control ability of the model are improved; parameters are optimized by designing a parameter search function and a search rule, and the search direction and step length are dynamically adjusted, so that the search process is more efficient, the anti-interference capability of the search process is enhanced, and local optimum can be jumped out.

The invention discloses a task scheduling method based on an improved bat algorithm in a mobile edge computing environment. The method comprises the following steps: acquiring user task information and edge server information, and acquiring a to-be-unloaded task sequence according to a user track; the user track, the user task information and the edge server information serve as input, an optimal solution of a current population is solved through a bat search algorithm, and each individual in the bat population is mapped to a corresponding task sequence; judging whether the task sequence needs to be subjected to local search or not, and if yes, performing local search by utilizing local sensitive hashing to obtain a global optimal solution after the local search; and according to the searched global optimal solution, dynamically updating the individual positions of the bat population until a set stop criterion is met, and outputting a final task scheduling decision. According to the method, the task scheduling quality is improved while the calculation efficiency is ensured, and the method has relatively high universality and expandability.

The invention discloses a hierarchical power electronic circuit layout method based on reinforcement learning, which belongs to the field of integrated circuits and mainly comprises three levels of element sequence generation, layout optimization and wiring optimization. The method comprises the following steps: firstly, analyzing the power flow direction of a power electronic circuit, and reasonably arranging an element layout sequence, so as to realize basic optimization from a top layer; then, on the basis of the element sequence, a convolutional neural network is used for extracting layout features, a graph convolutional neural network is used for extracting graph features of a circuit connection relation, specific design rules of the power electronic circuit are embedded into a reinforcement learning reward system, and a deep Q network (DQN) is used for achieving layout of elements; through reinforcement learning, the intelligent agent can summarize experience in trial and error without human intervention, and finally a reward maximized layout result is generated.

The invention discloses a multi-AGV path planning method based on constrained multi-objective optimization, and the method comprises the steps: dynamically dividing a whole population into three mutually exclusive subsets: an FNDS, an FNDS dominant subset and an FNDS-undominant subset, and not carrying out the balance convergence and constraint satisfaction in each subset according to the provided division of labor. The algorithm provided by the invention can effectively search feasible solutions with good diversity and convergence in a short time.

The invention belongs to the technical field of marine surveying and mapping, and discloses an airborne LiDAR sounding wave correction method and system. The method comprises the following steps: calculating wave parameters according to an Elfouhail spectrum model; using a KD-Tree accelerated K-nearest neighbor search method to determine a search neighborhood according to wave parameters so as to realize search of sea surface points in the neighborhood; and according to the efficient search result of the sea surface points in the neighborhood, constructing a multi-scale irregular TIN array sea surface model, calculating the normal vector of each beam of sea surface laser points with seabed reflection echoes, and performing ray tracing to correct wave errors to obtain accurate seabed point coordinates. According to the invention, seabed sounding errors caused by path deflection when laser enters water from air due to sea surface waves can be reduced, the sounding precision of the ALB system is improved, and powerful support is provided for ocean resource development, ocean environment protection and the like.

The embodiment of the invention discloses a stable adsorption configuration global search method based on similarity distribution and active learning. The method comprises the following steps: S1, constructing a knowledge-embedded high-throughput adsorption structure model; s2, constructing an adsorption structure small sample data set based on clustering stratified sampling; s3, in combination with the adsorption structure small sample data set and the Gaussian process regression model, establishing an adsorption energy prediction-oriented proxy model; and S4, training the proxy model, and searching a stable adsorption configuration. The embodiment of the invention discloses a stable adsorption configuration global search method based on similar distribution and active learning. By means of catalytic configuration high-throughput modeling containing knowledge embedding, small sample data set construction based on clustering stratified sampling, a small sample recommendation model based on Gaussian process regression and hybrid convergence criteria considering data distribution and agent model prediction, efficient searching of a stable adsorption configuration in a large structure space is achieved.

The invention discloses a multi-modal multi-target flexible job shop scheduling optimization method. The multi-modal characteristic of an FJSP is verified through experiments; designing a multi-modal solution archiving mechanism, and storing and analyzing a multi-modal solution in an optimization process; a non-dominated sorting genetic algorithm is improved, a combined crossover variation strategy is introduced, and a multi-modal solution in the algorithm operation process is searched. According to the method, MOFJSP is solved through NSGA-MSPM, and the algorithm provides various different solutions for the workshop scheduling problem by storing a plurality of high-quality multi-modal solutions, so that the flexibility and adaptability of production scheduling are improved.

The invention relates to a target detection performance curved surface progressive sampling method based on Gaussian process multi-core fusion. The invention relates to the technical field of target detection, in a target detection process, multi-level search space construction is carried out, and efficient kernel function combination and optimization are realized in a complex performance curved surface scene; a progressive sampling optimization strategy of a multi-scale collaborative adaptive kernel system is established, a kernel function combination structure and parameters are optimized step by step, and the fitting precision of a Gaussian process model to a target detection performance curved surface is improved. The method is mainly used for solving the problem of model performance evaluation under a multi-dimensional interference condition, and the performance evaluation efficiency and accuracy of the target detection model in a complex environment are improved by optimizing kernel function combination and parameter tuning.

The invention discloses a fusion positioning algorithm based on low earth orbit satellite Doppler measurement, DME and VOR, and relates to the technical field of satellite navigation, and the algorithm comprises the steps: obtaining Doppler frequency shift of a low earth orbit satellite, slant range information of the DME and azimuth angle data of the VOR, constructing a combined observation equation, and obtaining a corresponding Jacobian matrix; based on the geometric accuracy factor, channel and satellite selection is carried out through a genetic algorithm, DME, VOR and low-orbit satellites are optimized and selected, and an optimal navigation source combination is obtained; motion models in different flight modes are constructed, interactive multi-model Kalman filtering is utilized to dynamically estimate the state of the aircraft, and position information is updated in real time. Therefore, by the adoption of the method, real-time positioning of the aircraft under complex conditions is achieved, the anti-interference capability of the system is improved, and the overall positioning precision and stability of the system are improved.

The invention relates to the technical field of railway bridge crack detection and positioning, in particular to a steel plate crack positioning method based on a convolutional neural network and a particle swarm optimization algorithm, which comprises the following steps: drawing grid lines in a corresponding region of a steel plate, detecting acoustic emission signals of specific coordinate positions in the corresponding region of the steel plate based on a convolutional neural network model, obtaining a steel plate crack first positioning result; wherein the convolutional neural network model adopts a global mean pooling layer to execute average pooling operation on each channel in the two-dimensional feature map after convolution processing, and outputs a one-dimensional feature vector; or introducing an adjustable learning factor and an inertia weight into a particle swarm optimization algorithm, and carrying out traversal search on the acoustic emission signals of the corresponding areas of the steel plate in combination with the crossing thought of a genetic algorithm to obtain a second positioning result of the steel plate crack; and taking the first positioning result or the second positioning result as a final positioning result of the steel plate crack. According to the invention, automatic detection and accurate positioning of bridge fatigue cracks can be realized.

The invention discloses an attribute-based encrypted data sharing system supporting flexible change of a user. The attribute-based encrypted data sharing system comprises a central authority CA, generating and issuing public parameters, generating a first key component and an authority amount, and initiating attribute revocation; the attribute authority AA is used for generating a pre-decryption key, an attribute quantity and a new pre-decryption key, and updating a key component; the data owner DO provides a user public key, a first attribute set, an index, a blind keyword and an initial ciphertext; the proxy server PS generates a keyword search trap door and a final ciphertext, verifies the search request, downloads the corresponding final ciphertext for pre-decryption after the search request passes the verification, and outputs a pre-decrypted ciphertext; the cloud service provider CSP stores the final ciphertext; and the DU generates and sends a second attribute set, initiates a search request based on a keyword search trap door, and receives and decrypts the pre-decrypted ciphertext. The access control and sharing utilization of the data by the data owner are realized, and meanwhile, the forward security and instantaneity of the user after revocation are ensured.

The invention discloses a heterogeneous multi-core task scheduling method based on a whale optimization algorithm. The heterogeneous multi-core task scheduling method comprises the following steps: step 1, task information acquisition and model establishment; firstly, to-be-scheduled task information needs to be obtained, and the task information comprises task types, calculation amount, memory requirements and communication overhead; then, according to the architecture characteristics of the heterogeneous multi-core processor, establishing a task scheduling system model; 2, the number of white whale individuals is initialized and corresponds to a task scheduling scheme; 3, solving an individual with the highest fitness based on a white whale optimization algorithm, and taking the individual as an optimal solution; and 4, determining a final task scheduling scheme according to the optimal solution, wherein the final task scheduling scheme comprises a processor to which each task is allocated and an execution sequence. 5, the scheduling scheme is applied to the heterogeneous multi-core system, and task scheduling is executed. According to the method, the task scheduling efficiency is remarkably improved, and the overall task completion time is shortened.

The invention discloses a translation point configuration initial value design method based on sequential optimization, and belongs to the technical field of aerospace. The implementation method comprises the steps of designing an orbit of a translation point detector, namely, based on a circular restrictive three-body model, obtaining a periodic or quasi-periodic orbit near a translation point through differential correction, and then obtaining an orbit near the translation point under a high-precision ephemeris by using a multiple targeting method. And designing the orbit of the earth phase offset detector, obtaining an optimized initial value according to the characteristics of the instantaneous earth phase offset orbit, and performing optimization by taking the minimum phase angle variation as an index and utilizing a differential evolution algorithm. And taking a configuration stability index as an optimization objective, introducing a relaxation factor and a relative arrival factor, establishing a multi-objective optimization problem, and converting the multi-objective optimization problem into a multi-objective arrival problem. And an expected reaching target is given, high-precision numerical optimization is carried out, a configuration initial value with relatively high stability is obtained, and related technical problems in the translation point configuration application field are solved.

The invention discloses a fatigue simulation method for nonlinear energy dissipation of an asphalt mixture, which abandons a simplified hypothesis of a traditional linear damage model, and accurately depicts a residual bonding radius multiplier change rule of the asphalt mixture in an initial stage, a micro-damage stage, an accelerated damage stage and a damage stage through a piecewise function. According to the method, nonlinear mechanical behaviors of a material in different damage stages can be accurately captured, meanwhile, model parameters are accurately calibrated in combination with an ant colony optimization algorithm, a model parameter space is efficiently searched by simulating colony intelligent behaviors of ant colony foraging in nature, and an optimal parameter combination is found, so that the optimal mechanical behavior of the material is obtained. The simulation result is highly matched with the test result, the accuracy of fatigue life prediction is remarkably improved, the prediction error is effectively reduced, a more reliable theoretical basis is provided for the structural design of the asphalt pavement, and the service life of the pavement is prolonged.

The invention discloses a multi-UAV (unmanned aerial vehicle) collaborative search method imitating bird territory marks. The method comprises the following implementation steps: step 1, initializing a search environment; 2, unmanned aerial vehicle action strategy selection and position updating; 3, updating a search map and a territory smell map; 4, calculating a reward function and an observation matrix; 5, updating the reinforcement learning strategy network; and 6, outputting a search result. The method aims at solving the problem that multiple unmanned aerial vehicles search dynamic targets in an unknown environment, dynamic coverage of a target search area can be achieved in combination with a bionic model marked by a bird territory and an MAPPO reinforcement learning algorithm, efficient and intelligent search of multiple unmanned aerial vehicle dynamic targets is achieved, and the cooperation ability between the unmanned aerial vehicles in the search process is improved.

The invention belongs to the field of fire-fighting data processing, and particularly relates to an AI intelligent data processing method for the fire-fighting field, and the method comprises the steps: firstly, building a fire-fighting multi-association knowledge graph through collecting fire-fighting multi-field and multi-source data; secondly, analyzing semantic information and a scene background input by a user by adopting a configured multi-input intention reasoning model, and determining a main-collaborative knowledge sub-graph; 3, constructing a multi-graph joint reasoning model, fusing the main-collaborative knowledge sub-graph and semantic information input by the user, generating a user fire-fighting demand plan set, and continuously optimizing the model performance by constructing an optimal reasoning search path and a reasoning approximation loss function; and finally, according to real-time user input and scene feature space, a user-customized fire-fighting demand plan is quickly generated by using the trained multi-graph joint reasoning model, and the information is fed back to the fire-fighting multi-association knowledge graph, so that continuous updating and accumulation of knowledge are realized, and powerful support is provided for intelligent decision making in the fire-fighting field.

In order to solve the problem of a local optimal solution in the prior art, the invention provides a welding beam design optimization method and device based on a Bayesian optimization algorithm of variable selection and a medium. The optimization method comprises the following steps of building a welding beam design problem model, building a Gaussian process model, selecting variables through a roulette, optimizing a collection function and carrying out iterative optimization. The method has the beneficial effects that 1, the problem of local optimum is solved, and the Bayesian optimization can more effectively search a global optimal solution in a global range by establishing a Gaussian process model of a target function and combining an expectation improvement criterion; and 2, computing resources are saved, the computing efficiency is improved, and the next most potential point is intelligently selected through variable selection and acquisition function optimization for fitness value calculation, so that the number of evaluation times in the design process is reduced, the computing cost is saved, and the number of iterations is also reduced. And 3, the design flexibility is higher.

The invention discloses a pressure pipeline robot underground water supply pipeline leakage detection method based on a PSO-CNN-LSTM neural network algorithm, and the method comprises the steps: obtaining the monitoring data of a pressure pipeline robot, constructing a pipeline leakage data set, and dividing the pipeline leakage data set into a training set, a verification set and a test set according to a proportion; a PSO-CNN-LSTM model is constructed by using the pipeline leakage data set; a PSO-CNN-LSTM model is initialized, hyper-parameters are set, the training set is imported into the PSO-CNN-LSTM model, and model training is carried out; hyper-parameters are adjusted in sequence, a verification set data test model is introduced, change curves of loss values and accuracy rates of the model under different hyper-parameters are compared, and an optimal model is searched; and according to test set data, testing the optimal model, outputting various numerical evaluation indexes, and judging whether an expected value is reached or not. According to the invention, the leakage point in the pipeline can be accurately detected.