1197results about How to "Easy to identify" patented technology

The invention relates to an underwater acoustic target recognition system and method based on multi-modal depth feature fusion, and belongs to the field of underwater acoustic target recognition. The method comprises the following steps: firstly, performing preprocessing on an obtained underwater acoustic target original audio and associated metadata, and constructing a multi-modal data set; and then the constructed multi-modal sample is input into an identification model, the model extracts deep representation of each modal through a multi-branch feature coding network, depth alignment and complementary aggregation of different modal features are realized by using a cross-modal cross attention mechanism guided by potential query, and a target identification result is output based on a decision network of mixed experts. According to the method, experimental verification is carried out on two disclosed underwater acoustic data sets, the experimental result verifies the effectiveness of the multi-modal deep fusion and hybrid expert adaptive decision strategy adopted by the method, and through mining the complementary advantages of acoustic features and semantic priori, the multi-modal deep fusion and hybrid expert adaptive decision strategy is obtained. And the robustness and generalization ability of the underwater acoustic target recognition system in the strong-noise and multi-working-condition environment are remarkably improved.

The invention relates to the technical field of risk early warning, and provides an operation risk early warning method, device and system for substation operation and maintenance personnel and a medium. According to the implementation scheme, the method comprises the steps of training a behavior recognition model based on multi-modal historical data to obtain a target recognition model; based on the current operation data of the operation and maintenance personnel, performing risk identification on the current operation behavior represented by the current operation data through a target identification model to obtain an identification result; if the identification result is that the current operation behavior belongs to the dangerous behavior, triggering a multi-dimensional risk assessment mechanism to perform risk assessment on the current operation behavior to obtain a risk assessment result; and generating risk early warning information based on the risk assessment result. According to the embodiment of the invention, the intelligent early warning of the operation risk of the operation and maintenance personnel of the transformer substation can be realized, and the safety guarantee level of the operation and maintenance personnel during operation is improved.

The invention relates to the technical field of power distribution automation and intelligent power grid equipment, in particular to an FTU-based intelligent voltage regulation protection bypass cooperative control system, which comprises a state acquisition module, a state identification module, a self-adaptive disturbance judgment module, a voltage regulation cooperative module and a state reconstruction module, the system establishes an operation state vector model by synchronously sampling the voltage and current of a distribution line and the states of a circuit breaker and a bypass switch; judging line operation conditions according to vector state classification, and generating corresponding action priorities and time windows; unified scheduling and interlocking control are carried out on the voltage regulation unit, the protection unit and the bypass unit through the cooperative control execution module, and logic cooperation and state closed-loop updating of multiple control objects are achieved; according to the invention, time sequence coordination and intelligent linkage of voltage regulation, protection and bypass operation can be realized under complex working conditions, the voltage stability and the operation reliability of the distribution line are improved, the fault conflict probability is reduced, and the operation and maintenance intervention is reduced.

The embodiment of the invention discloses a control method and device of automatic cleaning equipment and related equipment, the automatic cleaning equipment comprises an equipment body and a mechanical arm arranged on the equipment body, the equipment body is provided with a first sensor, and the mechanical arm is provided with a second sensor. The mechanical arm is controlled to extend out of the equipment body, the automatic cleaning equipment is controlled to move in the working environment, and visual information is collected through a first sensor and a second sensor in the moving process; in response to the high-altitude area identified according to the visual information collected by the second sensor, the automatic cleaning equipment is controlled to move to the position below the high-altitude area; the mechanical arm is controlled to enter a high-position scanning posture, and visual information of a high-altitude area is collected through a second sensor; and the visual information collected by the first sensor and the second sensor in the mapping mode is fused to establish a map of the working environment, so that the navigation capability and the obstacle avoidance capability of the automatic cleaning equipment are improved.

The invention discloses a multi-modal contrast learning fault diagnosis method for a small sample scene, and the method comprises the following steps: carrying out the enhancement of a one-dimensional signal and two-dimensional image fused fault data set through physical simulation for the small sample scene with scarce industrial fault data, and constructing a positive and negative sample pair through a plurality of data enhancement strategies; based on heterogeneous multi-modal fault data, designing a double-flow encoder architecture of a time sequence branch and an image branch, extracting depth features and mapping the depth features to a unified feature space through a projection head; performing supervised contrast learning pre-training based on intra-modal and inter-modal dual contrast loss; supervision fine tuning is carried out based on multiple loss functions such as physical guidance, so that accurate diagnosis of equipment faults is realized in a small sample scene. According to the fault diagnosis method under the unbalanced sample and limited labeling conditions, the problem that a traditional data driving model depends on large-scale labeling samples is effectively relieved through supervised comparative learning and cross-modal information alignment.

The invention discloses an intelligent trash-cleaning machine based on AI visual identification, and belongs to the field of visual identification, the intelligent trash-cleaning machine comprises an AI visual identification module, an intelligent control system and a rotary trash-cleaning execution mechanism, the AI visual identification module is in communication connection with the intelligent control system, and the intelligent control system is electrically connected with the rotary trash-cleaning execution mechanism; through cooperative work of the AI visual recognition module and the intelligent control system, precise recognition and efficient cleaning of dirt in water are achieved, and compared with a traditional dirt cleaning mode, the system has remarkable advantages. The method is excellent in recognition capability, sewage disposal efficiency, response speed, adaptability and the like, and can be widely applied to pretreatment scenes of pump station water inlets, urban river channels, reservoir spillways, sewage treatment plants and the like. Popularization and application of the trash remover can bring good economic, social and environmental benefits, powerful technical support is provided for water area treatment, and the trash remover has wide market prospects and development potential.

The invention belongs to the technical field of network space security and social platform governance, and particularly provides an agent-driven active interactive social robot detection method, electronic equipment and a storage medium, and the method comprises the steps: obtaining original data of a to-be-detected account, and forming a structured data packet; constructing a preliminary screening evaluation model, and outputting a detection task state based on the structured data packet; generating an initial detection state; obtaining an interaction record; carrying out evidence extraction on the interaction record to obtain an evidence vector of the round; and performing sequential updating on the initial risk score and the uncertainty based on the evidence vector, and outputting a detection result when a stop condition is met. According to the method, an active interactive evidence obtaining mechanism of a detection agent is introduced, and on the basis of passive preliminary screening, probe interaction is automatically selected and initiated to obtain incremental evidence, so that the recognition capability of a high-simulation social robot is improved, and an auditing report containing key evidence and an interactive link is output.

The invention discloses a steel structural member local corrosion intelligent identification and evaluation method based on deep learning, and the method comprises the steps: obtaining a steel structure surface image through an imaging device, carrying out the feature extraction and corrosion region identification of an input image through a YOLOv12 semantic segmentation model which introduces a CBAM self-attention mechanism, and generating a pixel-level corrosion mask; the actual physical area of the corrosion area is automatically calculated based on an area calculation algorithm of conversion of mask integration and ground sampling distance; the residual bearing performance of the corroded component is evaluated in combination with the corrosion area ratio and the morphological characteristic prediction model; and finally realizing visual display and report output of detection results, area data and performance indexes through a graphical interface. According to the invention, full-process automatic processing of local corrosion of the steel structural member from detection, segmentation, quantitative measurement and calculation to member performance evaluation is realized, and closed-loop intelligent evaluation from visual detection to mechanical evaluation is realized.

The invention discloses an AI substitute inspection method and system based on multi-modal perception, and relates to the technical field of intelligent detection. According to the method, images, audios, environmental parameters and equipment state data of an inspection area are synchronously collected through a multi-modal sensor array, and after space-time alignment preprocessing, multi-modal features are extracted and input into a pre-trained fusion model to generate unified feature representation. The fusion model adopts a Transform architecture to realize cross-modal feature interaction, an anomaly detection algorithm is used to identify and classify anomalies, a confidence coefficient is calculated in combination with an evidence theory, and finally an inspection report containing anomaly positions, types and processing suggestions is generated. According to the invention, the information limitation of single-mode inspection is solved, the detection accuracy and robustness in a complex environment are improved, unmanned inspection in a high-risk scene is realized, the labor cost is reduced, the safety is guaranteed, and the method is suitable for automatic inspection in the fields of industrial equipment, public facilities and the like.

The invention relates to the technical field of information security, and discloses a terminal clipboard sensitive information cross-application leakage real-time interception method, which comprises the following steps of: registering a clipboard access hook function in a kernel layer or a system framework layer of a terminal operating system, and capturing a clipboard reading operation in real time; comparing an application program identifier corresponding to the captured reading operation with an application program identifier of a clipboard owner, and judging whether the access is cross-application access or not; when the access is determined to be cross-application access, triggering a clipboard content scanning engine to perform sensitive information identification on texts, images and file paths in a clipboard; and obtaining an evaluation result based on a preset sensitive information rule base and the dynamic strategy engine. A clipboard access hook function is deployed in a kernel layer or a system framework layer, millisecond-level capture and blocking of cross-application clipboard reading operation are achieved, and traditional extensive management based on application permission declaration is upgraded to refined management and control based on content risk identification.

The invention discloses an illegal unmanned aerial vehicle source tracing method and system based on visible light, thermal imaging and radio frequency fingerprint fusion. The method comprises the following steps: synchronously acquiring target visible light, a thermal infrared image and a radio frequency signal; extracting appearance, thermal radiation and radio frequency fingerprint features in parallel; multi-modal features are dynamically weighted and fused through an adaptive cross-modal attention fusion network (ACMAF-Net), and unmanned aerial vehicle model identification and individual association are completed; reconstructing a three-dimensional track of the unmanned aerial vehicle by using multi-sensor observation data; the possible positions of a take-off point and an operator are deduced by combining track features and geographic information. The system comprises a multi-mode sensing subsystem, a data processing and fusion subsystem, a traceability analysis subsystem, a display control storage subsystem and the like. According to the invention, through three-mode complementary fusion, the limitation of a single technology is overcome, all-weather and high-precision detection identification and effective traceability of the illegal unmanned aerial vehicle are realized, and the low-altitude security and protection capability is remarkably improved.

The invention provides a nucleic acid probe, a primer group, a kit, a detection method and application, and relates to the technical field of biology. The nucleotide sequence of the nucleic acid probe is as shown in SEQ ID NO. 1; wherein the 3'end of the nucleic acid probe is modified with MGB, and locked nucleic acid is modified at a base position corresponding to the difference between the swine fever wild strain and the swine fever vaccine strain in the nucleotide sequence. According to the nucleic acid probe, a specific sequence is utilized to precisely target a swine fever wild strain, and locked nucleic acid modification is introduced at a differential site, so that the recognition capability on single-base mismatch is remarkably enhanced, non-specific binding to a vaccine strain is thoroughly blocked, and false positive interference is eliminated; and 3 '-terminal MGB modification is matched to improve the binding stability and sensitivity, so that the efficient and accurate identification of the swine fever wild strain under the immune background is ensured.

The invention discloses an AI marking closed-loop method based on active learning and difficult case mining, and the method comprises the steps: carrying out the parallel calculation of uncertainty, representativeness and diversity three-dimensional value indexes after training an initial model, and carrying out the dynamic weighting screening of a high-value sample through a closed-loop feedback controller; difficult cases are recognized, DBSCAN clustering is adopted, similar sample expansion is retrieved, and the samples are stored in a dynamic pool; annotations are distributed, intelligent judgment is triggered for dispute samples after consistency verification, and labels are determined by fusing node reputation, model confidence and feature similarity; a mixed loss function is adopted for incremental training, and if the performance does not reach the standard, sampling, difficult examples and training parameters are adjusted in a linkage mode and then retry is conducted; the effectiveness of the difficult cases is evaluated after each round of iteration, the invalid difficult cases are eliminated, redundant clusters are combined, and the pool capacity is adaptively maintained; and the performance of the monitoring model is iteratively optimized until the standard is reached. According to the method, the labeling cost is reduced, and the boundary sample and long tail category recognition capability is improved.

The invention relates to the technical field of clinical medical treatment, and discloses a disease deterioration risk identification method and system based on depth time sequence clustering enhancement, and the method comprises the steps: 1, obtaining multi-modal clinical sequence data of a patient, including physiological indexes of a time sequence, a laboratory detection result, historical diseases and medication data; 2, performing feature extraction and classification on the patient sequences by adopting a knowledge enhanced sequence clustering method, and grouping the patient sequences according to future outcome distribution of the patient sequences; 3, enabling the model to quickly adapt to a prediction task of a new patient subgroup through a meta-training process; and step 4, based on the trained meta-model, carrying out rapid adaptation on the new patient subtype, and predicting the possibility that the new patient subtype has a deterioration event in a certain time window in the future. The method and the system can effectively learn the disease change mode of the patient under the condition of limited clinical data, improve the prediction accuracy of the new patient subgroup, and are especially suitable for clinical prediction scenes under the condition of small samples.

The invention belongs to the technical field of equipment fault intelligent diagnosis, and particularly relates to an inspection robot charging circuit fault early warning method and system. The method comprises the steps that high-frequency current signals in a charging loop are collected and preprocessed, and a standardized one-dimensional current signal sequence is obtained; constructing a relative position matrix by calculating an amplitude difference value of any two sampling points in the sequence, and representing the relative position matrix as a two-dimensional feature; inputting the two-dimensional features into a multi-scale feature extraction and fusion network, extracting time sequence features under different time scales in parallel, and fusing the time sequence features into feature vectors; based on the feature vector, identifying a fault type through a depth feature extraction and classification module, and evaluating a risk level in combination with a feature change trend; and finally, according to the diagnosis and evaluation result, outputting an early warning signal and triggering safety linkage control. According to the invention, early weak fault features can be effectively captured.

This invention provides an intelligent vibration temperature control device for concrete pouring in hydraulic engineering, relating to the field of vibration equipment technology. It includes a main vibrating rod, a lower connecting support rod, an external temperature control hose, and a temperature control storage tank. The lower connecting support rod and the external temperature control hose are arranged in a ring array on the outer side of the main vibrating rod. The external temperature control hose and the temperature control storage tank are interconnected. The tail end of the lower connecting support rod is rotatably connected to a middle connecting rod, and the tail end of the middle connecting rod is rotatably connected to the head end of an upper connecting support rod. The middle connecting rod, the upper connecting support rod, and the lower connecting support rod together form a V-shaped linkage mechanism and are arranged in a ring array outside the main vibrating rod. The external temperature control hose maintains a V-shaped bend simultaneously. The external temperature control hose extends and moves away from the main vibrating rod, expanding the temperature control influence range and improving temperature control efficiency. This solves the problem that the fixed, close-fitting temperature control pipeline for vibration can only cool a very small area of ​​concrete in the center of the vibrating rod, resulting in a small temperature control influence range.

The application discloses a comprehensive linkage alarm system for fire hazards and structure instability of timber buildings, and relates to the technical field of timber safety monitoring, which comprises the following modules: a multi-dimensional heterogeneous signal acquisition module for collecting data and constructing real-time state monitoring data sets; a structure spectrum benchmark modeling module for constructing a thermal-rigidity attenuation theoretical model; a thermal-frequency correlation analysis module for generating a thermal-frequency deviation degree index; a structure state determination module for identifying timber pyrolysis states and structure instability risks; a cascade response module for executing corresponding fixed-point spraying control, evacuation path planning and alarm output; and a self-adaptive feedback correction module for warehousing data and correcting thermal aging parameters. The application establishes a correlation model of thermal and rigidity by fusing thermal imaging and vibration data, effectively eliminates environmental wind load noise, realizes accurate identification of internal hazards of timber buildings, and improves the accuracy of building monitoring and the efficiency of emergency response.

The utility model relates to a kind of prepreg laying-up auxiliary devices, including material bearing assembly;The material bearing assembly includes cavity, the corner of the cavity is equipped with avoidance groove, the depth of the avoidance groove is same with the depth of the cavity;The inner wall of the cavity is equipped with at least one material taking groove and multiple limit grooves along its long side extension direction, the depth of the material taking groove is greater than the depth of the cavity;Multiple limit grooves are symmetrically set on the inner wall of the two sides of the cavity;The upper surface of the cavity is equipped with angle mark, and the angle mark includes at least one scale line for indicating the laying direction of prepreg.The prepreg laying-up auxiliary device of the application reduces the problems such as difficult to enter the mold and misplacement caused by size problem by controlling the size of laying-up blank;In addition, by matching the material cutting size with the cavity, the splicing gap or uneven lap phenomenon caused by artificial splicing is reduced, which is beneficial to size positioning when splicing prepreg.

The application discloses a tunnel or mine gushing water space-time prediction method and system coupled with a water power numerical model, and the method comprises the following steps: based on the identified and verified underground water numerical model, outputting multi-source data, complementing the missing measured data, quantifying the difference between fault and normal stratum permeability characteristics and coupling to the data system, and incorporating the tunnel or mine excavation space data. An LSTM-isolation forest-K nearest neighbor regression coupled model is constructed, and a multifunctional module is configured to realize common training of multi-scene data. The pretreated multivariate time series data is divided into a training set and a test set, hidden features are extracted through the coupled model, abnormal detection results are fused, a residual correction model is trained synchronously, and the hyperparameters and weights are adaptively optimized according to the multi-project prediction error feedback. Based on the trained coupled model, a window rolling strategy is adopted to carry out synchronous gushing water space-time prediction, residual correction is combined, and prediction data meeting the engineering precision is output. Reliable technical support is provided for engineering construction safety control.

The utility model relates to the technical field of bionic pressure sensors, in particular to a broadband wide piezoresistive sensing structure simulating scorpion threshold response characteristics, which comprises a threshold adjusting structure and a piezoresistive sensing unit. The threshold value adjusting structure comprises a flexible deformation cover and a threshold value sensing structure base, the flexible deformation cover covers the top end of the threshold value sensing structure base, the piezoresistive sensing unit is arranged in the threshold value sensing structure base, and a rectangular through groove is formed in the bottom of the threshold value sensing structure base; a threading hole is formed in the side wall of the threshold sensing structure base; the flexible deformation cover comprises a sheet, and the center of the lower surface of the sheet is integrally connected with an inverted-cone-shaped protruding block. The problems that the inherent contradiction exists between the sensitivity and the bandwidth performance of an existing piezoresistive sensor, in particular, structural instability under large signals is easily caused when the sensitivity is improved through a traditional design, and the response capacity to small signals is greatly reduced by increasing rigid support to expand the measuring range are solved.

This application relates to a wireless Jacquard needle block, a combing device, and a warp knitting machine. The wireless Jacquard needle block includes a connector, a control circuit, a drive circuit, and yarn guide needles. The connector includes a first power pin, a second power pin, a ground pin, and a signal pin. The control circuit connects to the first power pin, the ground pin, and the signal pin. The drive circuit connects to the second power pin, the ground pin, the control circuit, and the yarn guide needles. The first power pin is used to connect to a first power source powering the control circuit, the second power pin is used to connect to a second power source powering the drive circuit, and the signal pin is used to connect to a control signal. The control circuit outputs a drive signal based on the control signal, causing the drive circuit to control the operation of the yarn guide needles according to the drive signal. The lengths of both the first and second power pins are shorter than the length of the ground pin, enabling a reliable grounding connection to be established before power is connected when connecting to the backplate, effectively addressing transient overvoltages caused by hot-plugging and improving reliability.

The present application relates to a kind of preparation methods of essence.The problems that traditional process parameter adjustment relies on artificial experience and is difficult to realize cross batch self-adaption, multi-modal feature extraction, parameter-function-stage three-dimensional label matrix generation, graph neural network behavior atlas construction and expert strategy distribution constraint dual-channel strategy learning method are proposed.By process parameter and derived feature multidimensional fusion, time series causal strength analysis and action instruction simulation verification, optimization process parameter combination with explainable function synergistic relationship can be output.The technical scheme effectively realizes the automatic optimization of process control decision and the rapid self-adaption of model under new raw material batch, improves product consistency, quality stability and control energy efficiency.

The application discloses a baby crying sound recognition method for white noise equipment, relates to the field of audio processing and intelligent acoustic recognition, and comprises the following steps: collecting pure reference signals, first signals and second signals for synchronization and preprocessing, and obtaining mixed audio signals based on the first signals; calculating residual signals through a self-adaptive echo cancellation algorithm; calculating acoustic masking parameters based on the residual signals, the pure reference signals and environmental noise estimation; constructing a three-level recognition processing path comprising light feature detection, registered voiceprint comparison and multi-modal information fusion; selecting a recognition processing path based on the numerical range of the acoustic masking parameters, confirming the crying event of the target baby from the residual signals; and triggering corresponding graded alarms based on the crying event confirmation result. The three-level recognition path is scheduled through the acoustic masking parameters, multi-modal information is fused, and accurate and low-power baby crying monitoring under strong interference is realized.

The invention discloses an electronic nose gas classification model construction method and system, and relates to the technical field of gas sensing detection and artificial intelligence crossing. The method comprises the steps of performing fine adjustment on a pre-trained Transform encoder based on drift-free source domain data, constructing a source domain reference model and initializing an online updating component; in the real-time classification stage, after preprocessing and feature textualization conversion are carried out on target domain data, the drift mode type of the target domain data is judged by calculating feature distribution similarity, and representative samples are screened from current data; aiming at the new drift mode, training a new LoRA adapter; for a known drift mode, loading an existing LoRA adapter and carrying out lightweight fine tuning; and finally, obtaining a model for gas classification. According to the method, self-adaptive and lightweight online learning of sensor drift is realized, the calculation overhead and the storage requirement are reduced while the classification precision is ensured, and the long-term stability of an electronic nose system in a dynamic environment is improved.

The invention provides a post-processing federated long-tail classifier calibration method and device based on random noise driving, and the method comprises the steps: carrying out the multi-round federated learning training between a central server and a plurality of clients, and carrying out the aggregation to obtain a pre-training global model comprising a feature extractor and a classifier; at the central server side, freezing the feature extractor, and constructing a virtual balance data set based on random noise sampling and uniform label sampling; optimizing parameters of the classifier by using the virtual balance data set and a calibration loss function to obtain a calibrated classifier; and for a to-be-classified test sample, predicting the to-be-classified test sample by using the feature extractor of the central server side and the calibrated classifier. The method is applied to an image classification task in federated learning, classifier deviation caused by long-tail distribution can be effectively calibrated under the conditions that data privacy is completely protected and no auxiliary information is needed, and the recognition performance and the overall prediction calibration degree of the model on the tail category are remarkably improved.

The present application relates to the technical field of intelligent identification system of crop pests, in particular to a plant field pest fine-grained identification method, system, device and storage medium based on deep learning. The identification method provided by the present application is specialized in high-precision identification of real field scenes, and can provide technical support for important work such as future development of field inspection robot, automatic identification and monitoring system of field pests and the like. In addition to pest monitoring, the field biological safety test of genetically modified plants is gradually carried out at present, and by using the identification method provided by the present application, the dynamic change of farmland insect community can be quickly and accurately identified and predicted, so that the efficiency and accuracy of ecological investigation are greatly improved.

The utility model relates to the field of controller silica gel button, disclose silica gel button edge anti -warp reinforcing edge structure, including silica gel base plate, the top fixedly connected with silica gel button of silica gel base plate, the outside fixedly connected with anti -warp reinforcing mechanism of silica gel base plate, the outside fixedly connected with antiskid protection mechanism of silica gel button, the anti -warp reinforcing mechanism includes reinforcing tensile edge, the outside fixedly connected in the outside of silica gel base plate of reinforcing tensile edge, the outside of reinforcing tensile edge is equipped with metal protective cover edge. In the utility model, through fixing reinforcing tensile edge outside silica gel base plate, then the metal protective cover edge is connected with the inside of shell simultaneously, and the precise positioning and locking are realized by using anti -shift joint convex point, positioning recess, extension locking boss and fitting groove, form stable connection, in this way can effectively enhance silica gel base plate edge tensile strength, resist external collision scratch, restrain edge warping deformation.

The invention provides a molecular probe targeting EDB-FN as well as a preparation method and application of the molecular probe. The structural formula of the molecular probe targeting EDB-FN is shown in the specification, according to the molecular probe targeting EDB-FN, the probe is based on a cyclic peptide probe with Lys-Glu side chain lactam bridge conformation fixed, the connection mode of a DOTA chelating agent is optimized, and the clinical transformation bottlenecks that an existing probe is poor in stability, low in affinity, insufficient in targeting specificity, poor in diagnosis and treatment integration compatibility and the like are solved. The probe can be used for early diagnosis and molecular imaging research of tumors, and provides a reliable basis for tumor malignancy degree evaluation and treatment effect monitoring.

Methods, systems, and apparatuses are described herein for improving computer authentication processes through the generation of synthetic merchants. A plurality of different real merchant names may be received. The plurality of different real merchant names may be processed to determine one or more name elements. A request for access to an account associated with a user may be received. Based on the one or more name elements, one or more synthetic merchant names may be generated. Based on the one or more synthetic merchant names, synthetic transaction data may then be generated. A synthetic authentication question may be generated and presented to a user. A candidate response to the synthetic authentication question may be received. Based on the candidate response, access to the account may be provided.