82results about How to "Improve evaluation accuracy" patented technology

The invention belongs to the cross technical field of remote sensing data processing and soil environment monitoring, and discloses a multi-source remote sensing collaborative soil quality index inversion and comprehensive evaluation system and method, and the method comprises the following steps: obtaining different types of remote sensing data in a target area, and constructing a multi-source data set; the acquired multi-source remote sensing data are preprocessed, data noise and system errors are eliminated, and data consistency is ensured; performing multi-source remote sensing data fusion by adopting a spectrum-polarization-texture collaborative fusion strategy in combination with a multi-scale decomposition and weighted fusion model; based on the fusion data, constructing a machine learning inversion model, and inverting soil quality key indexes; and carrying out comprehensive evaluation on the soil quality by adopting an analytic hierarchy process-matter element extension model to realize grading of the soil quality. By adopting the multi-source remote sensing collaborative soil quality index inversion and comprehensive evaluation system and method disclosed by the invention, rapid, accurate, large-area and dynamic monitoring and comprehensive evaluation of soil quality are realized.

The application provides a cloud game evaluation method and medium based on space-frequency enhancement and sparse attention fusion, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a cloud game video stream to be evaluated, and extracting key frames and key region image blocks from the cloud game video stream; inputting the key frames into a pre-trained global feature extraction network to obtain global features, global shallow layer features, global middle layer features and global deep layer features; and inputting the key region image blocks into a pre-trained local feature extraction network to obtain local features, local middle layer features and local deep layer features. The method of the application can precisely capture global scene distortion and local high-sensitive region distortion of a cloud game video by constructing a multi-stage sparse fusion network and a space-frequency multi-level fusion network, while reducing model parameter quantity and calculation cost, meeting real-time inference and terminal lightweight deployment requirements of a cloud game scene.

The application provides a kind of to identify batch electronic product average life evaluation method, it is related to the technical field of reliability engineering and life evaluation, solve the technical problem that average life evaluation method in prior art is difficult to accurately evaluate the average life of the batch electronic product.The method comprises establishing a segmented life distribution model, the segmented life distribution model includes a first stage life distribution model and a second stage life distribution model separated by a segmentation point time T, and the segmented life distribution model satisfies the parameter constraint relationship at the segmentation point time T;Wherein, the first stage life distribution model adopts an exponential distribution model with failure rate λ;The second stage life distribution model adopts a Weibull distribution model;Based on the segmented life distribution model, the average life of the batch electronic product is calculated and obtained by applying the average life analytical expression.The application can improve the calculation efficiency and calculation accuracy.

This application discloses a method, apparatus, and semiconductor manufacturing system for evaluating pattern transfer accuracy. The method involves scanning the semiconductor microstructure to be evaluated to obtain surface contour data; comparing the surface contour data with target contour data of the semiconductor microstructure to obtain process error data; and generating feedback data on the pattern transfer accuracy of the semiconductor microstructure based on the process error data. The process error data directly reflects the accuracy of the structural dimensions formed by image transfer during the production process. The feedback data can be used to calibrate the deviation between the actual pattern and the target pattern of the semiconductor microstructure, which helps improve the accuracy and real-time performance of pattern transfer accuracy evaluation. It also facilitates further evaluation and analysis of pattern transfer accuracy using the feedback data, thereby enabling visual localization of the root cause of pattern transfer problems and improving the practicality of pattern transfer accuracy evaluation.

The invention discloses a lithium ion battery health state evaluation method and equipment. The method comprises the following steps: presetting a cell attenuation rate matrix in a battery management system, wherein the matrix defines single-circle capacity attenuation rates under different temperature and discharge depth combinations; acquiring the duration time and the corresponding discharge depth of the target battery in different temperature intervals in the complete time period; calculating cumulative capacity attenuation according to actual operation data and the matrix; and determining the current SOH according to the initial capacity sum. According to the method, simulation and actual measurement are combined to construct a high-precision matrix, a dynamic look-up table accumulation algorithm is utilized, high-precision and low-cost online evaluation of the SOH of the battery under complex variable working conditions is achieved, and the problems that a traditional method is insufficient in precision and high in calculation complexity in a dynamic scene are effectively solved.

The invention discloses a sodium-ion battery pack health state online evaluation method and system, and the method comprises the following steps: S1, constructing a sodium-ion battery aging model, collecting and preprocessing pack operation data, and generating a data sequence; s2, calculating a feature entropy value, adjusting the state of the liquid neural network, and extracting a fusion feature sequence; s3, performing path signature transformation on the fusion feature and the simulation sequence, calculating a structural deviation, and generating a deviation sequence; s4, updating model parameters by using particle filtering, and reasoning a capacity estimation value to form a capacity sequence; s5, performing Loess fitting and smooth filtering on the capacity sequence to generate a capacity estimation curve; and S6, evaluating the Pack health state according to the capacity estimation curve, and outputting an online result. According to the method, digital twin modeling, a liquid neural network and the like are fused, and the method has the advantages of high evaluation precision, high response speed and high adaptability.

The present application belongs to the technical field of photovoltaic equipment monitoring fault early warning, and relates to a photovoltaic equipment cluster benchmarking analysis and implicit fault early warning method and system. The system collects basic parameters and implicit associated data of photovoltaic equipment through a data acquisition module to ensure data comprehensiveness and timeliness. Through a data fusion module, the collected data is processed and fused to generate a standardized data sequence. Through a cluster benchmarking module, the photovoltaic equipment is divided into different clusters, a group health benchmark is constructed, and the model accuracy is improved through individual difference calibration. Through a dynamic early warning module, early warning information is generated, and a basis is provided for subsequent fault troubleshooting. Through an intelligent operation and maintenance module, early warning is prioritized and an operation and maintenance scheme is generated, which is pushed to operation and maintenance personnel for optimization guidance. The present application can realize accurate benchmarking analysis and implicit fault early warning of photovoltaic equipment clusters, and reduce the failure rate.

The invention provides a national and provincial road bridge heavy-load full-link cooperative monitoring and control system and method, and the system comprises a heavy-load source docking and prediction module, a dynamic deployment and control module, a multi-source data fusion and damage evaluation module and a cooperative management closed-loop module which are connected in sequence. Through docking enterprise transportation data and predicting a time window for a vehicle to arrive at a bridge, a sensing device is actively scheduled to carry out accurate monitoring according to the time window; through fusion of vehicle load and bridge structure response data, the degree of damage caused by overload is quantitatively calculated based on highway bridge engineering specification logic, and targeted risk assessment is performed on special bridges such as single-column piers and low-rating bridges. And finally, automatically generating a graded early warning and repairing scheme according to an evaluation result, and linking multiple departments such as traffic, maintenance, emergency and the like to form a disposal closed loop. According to the invention, full-link cooperation from the source to management is realized, the problems of monitoring blind areas and disjunction of damage attribution and response are solved, and the bridge heavy load safety prevention and control capability is remarkably improved.

The invention relates to the technical field of artificial intelligence, and provides a mountain wind energy resource dynamic evaluation method and device, equipment and a medium, which can perform attention fusion on multi-dimensional initial data, so that fusion features consider feature importance difference and comprehensiveness. A mountain wind energy evaluation data set with a wind energy power density label is constructed, and a basis is provided for supervised training of the model; the state space completely covers core variables influencing mountain wind energy evaluation, and the problem that a traditional model is insufficient in state space dimension and cannot represent a complex coupling relation is solved. The action space is in non-static output, so that the adaptive capacity of the model is improved; the reward function solves the technical problem that precision and efficiency cannot be considered at the same time; a dual experience playback mechanism improves the evaluation precision of the model on a complex wind field scene; the target network delay updating strategy enables the target network to maintain parameter stability in an iteration period, and the problems of training oscillation and slow convergence caused by real-time fluctuation of target return are solved.

This application provides a method and apparatus for determining blood flow velocity in a blood vessel, comprising: acquiring an initial contrast image and a target contrast image after injecting a contrast agent; comparing the initial contrast image and the target contrast image to determine the length of the blood vessel through which the contrast agent flows; determining the uniformity of the contrast agent in the main vessel region and / or the visualization of microvessels in the background region of the target contrast image based on the contrast agent concentration in the target contrast image; correcting the blood vessel length based on the uniformity of the contrast agent and / or the visualization of microvessels to obtain a corrected blood vessel length; and determining the blood flow velocity based on the corrected blood vessel length. This reduces calculation errors caused by the imaging angle of the contrast image, the shape of the blood vessel, or blood vessel bifurcation, thereby improving the accuracy of blood flow velocity assessment.

This invention discloses a method for detecting the aging state of asphalt pavement by integrating hyperspectral imagery and deep learning, relating to the field of deep learning technology. The method includes: acquiring and preprocessing hyperspectral images of the target road section to obtain surface reflectance images; dynamically identifying and masking vehicles and their shadows in the images using a spectral angle matching method to extract effective pixel spectral datasets; performing hybrid pixel decomposition on the effective pixel spectral datasets using a non-negative matrix factorization algorithm to obtain asphalt endmember spectra and aggregate endmember abundance maps; inputting the asphalt endmember spectra into a pre-trained oxidation degree assessment model to output asphalt oxidation degree values; overlaying the aggregate endmember abundance map with near-infrared band images and inputting it into a pre-trained exposure rate segmentation network to output a binary mask of aggregate exposure areas and calculate the aggregate exposure rate. This method solves the technical problem that existing technologies cannot quickly and non-destructively detect the aging state of asphalt pavement.

The invention discloses a subjective and objective combination weighting evaluation method based on variance optimization, and the method comprises the steps: firstly evaluating the relative importance of each index, and obtaining the subjective weight of each index; constructing a sample-index matrix, calculating the information entropy value of each index, obtaining the weight of each index based on the information entropy, removing redundant indexes through the calculation of the index correlation degree and the correlation degree between the indexes, achieving the de-overlapping, calculating the absolute correlation degree of the indexes, and carrying out the normalization to obtain the objective weight of the indexes; setting subjective and objective weight coefficients, carrying out linear addition processing on subjective and objective weights, and on the premise that the sum of constraint condition coefficients is 1, enabling the variance to be as small as possible to obtain the optimal subjective and objective combined weight of each index; and judging subjective and objective weights of the indexes and the consistency of sorting arrays, and determining final weights of the indexes. On the basis of keeping the advantages of the original subjective and objective evaluation method, the advantage complementation of subjective weighting and objective weighting is realized, and the evaluation effect of the evaluation model is improved.

The application discloses a kind of electroencephalogram signal evaluation methods based on multi-modal data, by obtaining the same preset sampling time period within the electroencephalogram signal to be evaluated, electrooculogram signal, photoplethysmographic signal and inertial measurement unit signal.According to electrooculogram and the electroencephalogram signal to be evaluated, the electrooculogram corresponding saccade artifact probability index is obtained.According to photoplethysmographic, the photoplethysmographic corresponding pulse timing feature index is obtained.According to inertial measurement unit signal, the motion intensity index corresponding to inertial measurement unit signal is obtained.The saccade artifact probability index, pulse timing feature index and motion intensity index are weighted, and the evaluation result of the electroencephalogram signal to be evaluated is obtained.By this way, the comprehensive influence of various different interference sources on electroencephalogram can be fully considered, the real usability of electroencephalogram is accurately reflected, and the evaluation accuracy of electroencephalogram is improved.

This invention discloses a parallel collaborative evaluation method for large and small models based on short-term confidence scores, belonging to the field of federated learning outcome quality evaluation in computing power networks. The method includes the following steps: generating short-term evaluation confidence scores based on the short-term evaluation of the small model; having the large model evaluate the evaluation indicators with low confidence scores in the short-term evaluation confidence scores; and simultaneously, having the small model supplement the remaining content of the short-term evaluation, thus achieving parallel collaborative evaluation of large and small models. This invention, through parallel collaboration between large and small models, can fully utilize the high accuracy of the large model and the low resource consumption of the small model, solving the resource idleness problem of serial collaboration between large and small models and improving the efficiency and accuracy of the evaluation.

The invention discloses a method and system for quantizing the influence of temperature increase on farmland emission reduction potential, and relates to the technical field of crossing of agricultural ecology and climate change, and the method comprises the following steps: S1, collecting multi-dimensional temperature increase characteristics and basic data, dividing farmland regions and crop growth periods, extracting temperature increase characteristic parameters, and calculating the temperature increase characteristic parameters; greenhouse gas emission, soil, crop and meteorological basic data are synchronously collected; s2, contrast case matching and coupling model construction: matching a same-period contrast case based on a random forest algorithm, measuring and calculating a temperature increase response coefficient, and introducing a time-dependent attenuation coefficient and a region regulation factor to construct a multi-factor coupling quantification model; s3, emission reduction potential dynamic quantification and scene prediction; according to the method and the system for quantifying the influence of the temperature increase on the farmland emission reduction potential, the dynamic influence of the temperature increase on the farmland emission reduction potential is accurately captured, the shortages of the time effect and the regional suitability in the prior art are effectively made up, and the evaluation precision is remarkably improved.

The application discloses a kind of equivalent inertia evaluation methods of low-voltage distribution area, it is related to electric power distribution field, by obtaining the distributed multi-dimensional characteristic parameter of low-voltage distribution area, carry out variation modal decomposition and filter effective modal data and carry out superposition processing to obtain multi-dimensional characteristic reconstruction vector, to multi-dimensional characteristic reconstruction vector carry out Kalman filter correction, and based on the best state of Kalman filter correction carries out vector derivation to obtain multi-dimensional physical quantity derivative value, obtains the equivalent conductance and equivalent susceptance of low-voltage distribution area and constructs full-coupling impedance identification model, to multi-dimensional physical quantity derivative value carries out dynamic impedance identification processing, obtains dynamic impedance parameter, according to equivalent conductance and equivalent susceptance carries out internal equivalent frequency calculation, based on calculation carries out inertia parameter differential observation and evaluation processing, obtains the equivalent inertia evaluation data of low-voltage distribution area. With the effect of realizing the accurate quantification of low-voltage distribution area inertia, provide decision basis for frequency stability control.

The invention provides a multi-modal vision-based crack risk assessment method and system after a vehicle passes through a bridge, relates to the field of bridge detection, and solves the technical problem that the crack risk assessment is inaccurate due to the fact that the prior art only depends on static crack width risk judgment and does not fuse response characteristics such as a dynamic expansion rate after the vehicle passes through the bridge. The method comprises the following steps: constructing a cross-modal feature map based on an obtained multi-modal data sequence; inputting the cross-modal feature map into an improved U-Net + + lightweight segmentation network, generating a crack binary segmentation mask, and calculating the width of the crack based on the crack binary segmentation mask; based on multi-view stereoscopic vision and depth sensor data, a three-dimensional point cloud model of the crack is reconstructed, the depth of the crack is calculated, and the dynamic expansion rate and width variation of the crack after the vehicle passes through the bridge are calculated; and calculating a risk index of the crack based on the crack parameter. The method is used in the bridge crack risk assessment process.

The invention provides an evaluation method and device for a time-of-use electricity price making policy, electronic equipment and a storage medium. The method comprises the following steps: acquiring historical load data in a reference period and to-be-evaluated load data in an evaluation period; the reference period and the evaluation period have homogeneous comparability; constructing an undetermined reference load curve and a to-be-evaluated load curve according to the historical load data and the to-be-evaluated load data; adjusting the to-be-determined reference load curve by using the load adjustment coefficient to obtain a reference load curve; and according to the user industry response characteristics and the reference load curve, analyzing the to-be-evaluated load curve, and calculating the following indexes: a net load transfer amount, a peak-valley optimization effect index, a response stability index and an industry differentiation evaluation matrix. According to the embodiment of the invention, the multi-dimensional reference load curve is constructed and the load adjustment coefficient is introduced, so that the interference of external factors such as natural fluctuation and seasonal change of the total power consumption on the evaluation result is effectively eliminated, the evaluation precision is high, and the anti-interference capability is high.

The invention discloses an automatic identification and intelligent evaluation method for a primary frequency modulation disturbance event of a thermal power generating unit, and the method comprises the steps: collecting the operation data of the thermal power generating unit, and constructing a multi-dimensional time sequence data set containing the active power, the power grid frequency and a control instruction; performing abnormal value elimination and high-frequency noise suppression processing on the multi-dimensional time sequence data set to obtain a processed multi-dimensional time sequence data set; constructing an LSTM recognition model fused with the attention mechanism, inputting the processed multi-dimensional time sequence data set into the LSTM recognition model fused with the attention mechanism, and outputting a recognition result after training; based on the recognition result, the amplitude, response delay and overshoot index of the disturbance event are extracted, a comprehensive index is calculated, and the multi-dimensional time sequence data set, the recognition result and the evaluation index are stored and visually output. According to the method, the LSTM model of the attention mechanism is fused, the disturbance recognition accuracy is improved, and the algorithm misjudgment rate is reduced.

The invention discloses a cascade dam group system service risk matrix type evaluation method, which comprises the steps of collecting data information of a drainage basin where a cascade dam group system is located to construct a deep learning model, and performing dam break flood routing calculation; calculating a system failure risk rate of each unit dam, quantifying a risk transfer coefficient between adjacent unit dams, constructing a state transition probability matrix and a state transition loss matrix of the cascade dam group system, calculating a state transition risk matrix of the cascade dam group system, and obtaining a total risk rate of the system and three types of total risks of life, economy and social environment; the corresponding total loss is evaluated; and finally carrying out cascade dam group system risk rating. The method has higher calculation efficiency and precision, and can provide richer and more comprehensive calculation results; according to the system risk quantitative grading standard and method, theoretical preciseness is considered, meanwhile, higher engineering applicability and practical operability are achieved, and higher practical application value is shown.

This application provides a learning status assessment method, system, and device based on big data analytics, relating to the field of big data analytics. The method includes: collecting multi-source learning behavior data of students to construct individual knowledge graphs; collecting students' resting physiological signals to construct a physiological signal database; using the individual knowledge graphs as federated nodes and combining them with the physiological signal database to construct a group knowledge graph federation; constructing a teaching progress timeline based on knowledge points and calculating the progress offset between the student's current learning progress and the class's teaching progress; comprehensively evaluating the individual knowledge graphs, the group knowledge graph federation, and the progress offset to obtain a multi-dimensional comprehensive evaluation report; and using a multi-objective optimization algorithm to generate personalized learning plans and push them to students. This solves the technical problems of one-sided, subjective, and inaccurate learning status assessment results caused by a single evaluation dimension and neglect of the dynamic influence of class teaching progress.

The invention discloses a novel energy system safety risk analysis method and device based on carbon-energy coupling, and the method comprises the steps: inputting energy data and a carbon emission factor, and building a carbon-energy coupling dynamic model; generating typical risk scenes such as energy price fluctuation and extreme weather, and reducing to obtain a simplified scene set; then an upper-lower layer coupled risk quantification model is constructed, the upper layer focuses on energy supply cost and carbon emission optimization, and the lower layer focuses on power system operation cost minimization; and finally, solving based on a Monte Carlo method, and outputting a multi-dimensional risk assessment index. According to the method, the problem that multi-field risk interaction is difficult to describe by a traditional method is solved, accurate quantification of the multi-scene risk of the provincial novel energy system is realized, and scientific support is provided for provincial energy safety decision.

The application belongs to the technical field of aerospace manufacturing, and discloses a high-performance ceramic matrix composite functional surface for hypersonic transition regulation and control. Based on a numerical simulation method, a hypersonic cone transition process of a microstructure functional surface with different microstructure parameters is predicted, and the influence of the microstructure parameters on the transition position and aerodynamic drag is obtained. The microstructure functional surface design is completed by comprehensively considering the ceramic matrix material manufacturing constraints. The corrugated wall microstructure is manufactured by ultrasonic vibration assisted grinding, and the micro-hole array is processed by femtosecond laser. According to the segmented integrated manufacturing concept, a plurality of simulation cone sample pieces with the microstructure functional surface are processed. The simulation cone sample pieces with the microstructure functional surface are tested in a wide speed range in a hypersonic wind tunnel. According to the wind tunnel test data and the results of the numerical simulation, a high-fidelity data set and a low-fidelity data set are established, a multi-fidelity surrogate model is constructed, the microstructure parameters are subjected to multi-objective optimization, and an optimal solution set is obtained.

The invention relates to the technical field of carbon dioxide geological sequestration, and discloses a carbon dioxide geological sequestration quantity grading and evaluating method, system and device and a medium. A carbon dioxide sequestration efficiency coefficient influenced by process technical conditions and a carbon dioxide sequestration economic efficiency coefficient influenced by policies, laws and regulations and economic conditions are considered, and progressive carbon dioxide sequestration quantity evaluation is performed on a to-be-evaluated region, so that the geological carbon dioxide sequestration quantity can be evaluated more objectively and systematically; the evaluation precision of the geological storage amount of carbon dioxide is greatly improved, and effective and reliable data guidance is provided for development of geological storage work of carbon dioxide.

The application relates to the technical field of food processing equipment control, and discloses a food production equipment parameter self-adaptive regulation and control system and method, which comprises an absolute value encoder, a plurality of physical temperature zones and a main control unit. The main control unit divides continuous material into virtual material slices, updates absolute spatial coordinates and instantaneous linear velocity; obtains environmental temperature and circulating fan operation frequency to calculate the actual dynamic heat enthalpy accumulation of the slices; when the slices reach the end of the current temperature zone, generates a feedforward compensation opening increment for the downstream temperature zone according to the heat enthalpy error; establishes a trigger point based on time hysteresis parameters and linear velocity to issue the increment in advance; and when issuing, expands the static dead zone constant of the current temperature zone controller into a dynamic dead zone threshold value according to a cross-coupling coefficient to freeze output change. The application solves the problems of heat response lag and airflow coupling interference of the multi-temperature zone equipment, and prevents control oscillation.

The invention discloses an intelligent evaluation and optimization method and system for a safety gap of a power distribution network line. The method comprises the following steps: collecting multi-source sensing data of a target operation area in real time; carrying out fusion processing on the multi-source sensing data, constructing a three-dimensional space environment model containing semantic information of an electrified body, a grounding body and an operation instrument, and extracting real-time geometric gaps among objects; performing dynamic safety risk assessment based on a pre-stored safety standard library and the real-time geometric gap; and in response to a result of the dynamic safety risk assessment, automatically planning at least one recommended operation scheme meeting dynamic safety constraints in the three-dimensional space environment model, the recommended operation scheme comprising a motion path of an operation apparatus and / or an operation mode of an operator. According to the scheme, the crossing from passive warning to active navigation is realized, and the safety, accuracy and efficiency of non-power-cut operation are fundamentally improved.

The invention discloses a visible light remote sensing building damage assessment method based on hybrid visual state space and ordinal regression, and relates to the technical field of remote sensing image processing and disaster assessment, and the method comprises the steps: obtaining pre-disaster and post-disaster high-resolution visible light remote sensing images of a to-be-assessed region; inputting the pre-disaster image and the post-disaster image into a mixed feature coding network, extracting local visual features of a building through a convolutional neural network module, establishing a global spatial dependency relationship based on the local visual features by a visual state space module, and generating hidden state features containing physical damage semantics; inputting the hidden state features into an ordinal number state decoding network, and generating a continuous damage intensity diagram through single-channel continuous real number space projection; and outputting a building damage assessment result based on the continuous damage intensity graph. According to the method, the recognition precision of seriously damaged and collapsed buildings is remarkably improved, and the spatial smoothness and the calculation efficiency of an evaluation result are ensured.

This invention relates to the field of cable operation and maintenance, aiming to solve the problems of traditional cable insulation assessment neglecting multi-factor coupling and low accuracy of lifespan prediction, and to provide a precise early warning system. This invention includes modules for data acquisition, improved multi-dimensional coupled aging assessment, optimized lifespan decay prediction, early warning output, and data storage. It collects multi-dimensional electrical and environmental parameters through sensors, quantifies the aging level through coupled modeling and dynamic weight calculation, predicts the remaining lifespan based on an improved Arrhenius model and time-series residual correction, and outputs three levels of early warning through multiple channels. This invention improves the accuracy of aging assessment and lifespan prediction, supports cable operation and maintenance decisions, reduces fault risks, and is applicable to power system cable insulation condition monitoring scenarios.

This invention relates to the field of machine tool condition monitoring and health assessment technology, specifically to a feature analysis method and system for ranking machine tool health based on acoustic emission technology. The method involves: cleaning the raw acoustic emission signals collected by multi-channel acoustic emission sensors along the X, Y, and Z axes of the machine tool, removing invalid data, and generating mean and fluctuation sequences respectively; extracting four core feature indicators based on the preprocessed mean and fluctuation sequences; dynamically allocating sensor weights to integrate the indicators according to the test focus object, and after minimization and normalization, calculating and ranking the health score based on weights trained by machine learning. This invention enables accurate quantitative assessment and batch ranking of machine tool health status, supporting intelligent operation and maintenance throughout the entire lifecycle of the machine tool.