59 results about "Exponential weighting" patented technology

The invention relates to the technical field of energy system control, and discloses a distributed cooperative fault-tolerant control method for a multi-energy-station cooling and heating system, and the method comprises the steps: enabling each energy station to measure and calculate a residual error based on a physical model and a sensor, forming a normalized health index, and carrying out the neighborhood broadcasting; carrying out robust anomaly judgment by utilizing a neighborhood median and a median absolute deviation, and isolating an abnormal site; cost coefficients are automatically generated for available sites according to equipment maneuverability, and control redistribution of minimum disturbance is solved and implemented through distributed consistency optimization; an exponential weighting updating mechanism of a residual error sequence is adopted to carry out online self-adaption on a noise baseline and a judgment threshold value so as to realize closed-loop self-calibration; when sensor abnormity, equipment errors or individual station faults occur in the multiple energy stations which work cooperatively, distributed rapid identification is achieved, faults are isolated, energy supply is redistributed with minimum disturbance, system control requirements are met, and equipment safety constraints are kept.

The invention discloses a multi-source navigation data fusion method for an unmanned loader, which comprises the following steps: data acquisition: acquiring environmental perception data of satellite navigation, an inertial measurement unit IMU, a wheel type odometer and a laser radar and camera in real time; performing space-time synchronization preprocessing, realizing multi-source data time synchronization, unifying environment sensing data to a body coordinate system, eliminating abnormal data, and complementing missing data; carrying out dynamic weight calculation, establishing an error model of each sensor, and adjusting a fusion weight by using an error reciprocal exponential weighting method; layering fusion is carried out, a satellite and an IMU are fused through bottom-layer extended Kalman filtering (EKF), a laser radar and a high-precision map are fused through middle-layer ICP, a middle-layer result and a wheel type odometer are integrated through high-layer federated filtering, and high-precision fusion is achieved; and outputting and optimizing a result, outputting navigation data, performing closed-loop optimization, monitoring the health degree of the sensor, and executing redundancy switching when a fault occurs. The system comprises a corresponding processing unit, and a storage medium stores a program for executing the method.

The invention relates to the technical field of water, wind and light storage combination, in particular to a wind, light and water storage combination robust tracking method and device and a medium, which can uniformly represent the output uncertainty of extreme renewable energy sources and quickly map a safe feasible region in a high-dimensional scheduling space. And safe and economic cooperative control of the hydroelectric generating set, the reservoir water level and the energy storage resources within the resolution ratio of 15 minutes is realized. Firstly, unified normalization and sliding window time embedding are performed on wind speed, irradiation and load sequences in a data layer, and a dynamic error envelope is constructed through exponential weighting of a high-tail sample, so that a measurable extreme interval is directly obtained in an original observation space. And then introducing a physical climbing limit and a unit inertial constraint, sequentially expanding a disturbance scene tree on nodes at two ends of the error envelope, carrying out error envelope-physical mapping coupling modeling, enabling the system to keep time-space correlation, and directly incorporating unit dynamic characteristics in a scene generation stage.

The invention discloses an intelligent evaluation system and method for knowledge base question and answer application, and relates to the technical field of artificial intelligence, natural language processing and multi-agent collaborative systems. The system comprises a data preprocessing module, a data synthesis module, a data screening module, a data scoring module and a self-adaptive weight adjustment module. The data preprocessing module carries out data preprocessing on the enterprise original document and generates a data knowledge base; the data synthesis module adopts a large language model to perform repeated question and answer on each paragraph of the data knowledge base to generate question and answer pairs; the data screening module screens out high-quality samples through a screening agent collaborative screening MACA framework mechanism; the data scoring module performs question and answer scoring through a scoring agent collaborative scoring MACA framework mechanism; and the adaptive weight adjustment module dynamically updates the weight of each screening scoring dimension by adopting an exponential weighted moving average algorithm.

The invention provides a multi-modal feature fusion identification method and device for partial discharge of a high-voltage cable, and relates to the technical field of multi-modal feature fusion of partial discharge of the high-voltage cable. Multi-modal time sequence data of three-phase current, temperature and voltage to ground of the high-voltage cable are collected and subjected to preprocessing and time alignment to form a multi-dimensional input matrix; a physical relation matrix based on cable physical characteristics is constructed, physical constraints are injected into data through feature modulation, fused feature representation is generated, features are input to an LSTM auto-encoder trained by normal data, a sequence is reconstructed, a reconstruction error is calculated, finally, an exponential weighting algorithm is adopted to accumulate an error time sequence to obtain a comprehensive abnormal score, and the comprehensive abnormal score is obtained. And through comparison with a dynamic threshold value, accurate identification and partial discharge alarm of the insulation state abnormity are realized.

The invention relates to a YOLOv4-based optimization method for rapid identification and detection of a sowthistle obstacle, and the method comprises the steps: initializing an anchor frame through employing a K-means algorithm, generating an anchor frame with higher scale adaptability, and enabling a model to be more suitable for the detection of slender sowthistle; aiming at network structure optimization of network scene particularity, cutting far all-grass of Chinese ixeris and near small all-grass of Chinese ixeris, and only stably detecting a close shot existing in each frame of image; in the optimization of a pooling mode, pooling is carried out by adopting an exponential weighted average filtering mode so as to reserve all useful information as much as possible. According to the method, the YOLOv4 is adopted to accurately detect the all-grass of Chinese Ixeris, optimization is carried out in three aspects of priori frame reclustering, a pooling mode and a network structure, and the optimized YOLOv4 can accelerate a target detection process, so that the method is suitable for real-time application, the use of calculation and memory resources is reduced, the method is suitable for an embedded system or equipment with resource limitation, and the detection efficiency of the all-grass of Chinese Ixeris is improved. The accuracy of target detection is not damaged as much as possible, and high-quality recognition of the target is kept.

The invention discloses a motor and a control method and device thereof, a storage medium and a computer program product, and the method comprises the steps: injecting a pulsating voltage with a preset frequency into a d-axis part outputted by a PI controller in a current loop at a zero-speed starting stage and a low-speed operation stage of the motor; in the whole stage of the motor, three-phase current of the motor is obtained; in the whole stage of the motor, in a current loop and a speed loop, according to the three-phase current of the motor, a full-order observation module, a phase locking module and a switching function module are utilized to obtain a rotor position estimation value and a rotating speed estimation value of the motor; and in the whole stage of the motor, starting and running of the motor are controlled according to the rotor position estimation value and the rotating speed estimation value of the motor. According to the scheme, the improved high-frequency injection algorithm is combined with the full-order observer based on the back electromotive force, and the exponential weighting function is adopted to smoothly switch the position and the speed of the motor rotor estimated by the high-frequency injection algorithm and the full-order observer, so that the complexity and the cost are reduced, and the reliability is improved.

The invention discloses an elevator prediction scheduling method and system based on user habit self-learning, and the method comprises the steps: carrying out the statistical analysis based on historical elevator taking event data, and judging whether a floor combination meeting a preset condition exists or not; combining all the independent floors with the floors meeting the preset conditions to serve as a self-learning unit; for each self-learning unit, updating the elevator calling probability of the respective learning unit by using an exponential weighted average algorithm; when the elevator does not have the real-time task, all elevator calling probabilities corresponding to the current time window are inquired, and the self-learning units meeting the triggering condition are extracted as a target candidate set; calculating an optimal pre-stop layer by utilizing a mathematical model based on the target candidate set, and calculating a comprehensive probability corresponding to the optimal pre-stop layer; and calculating an income evaluation function based on the comprehensive probability, and generating an instruction to drive the elevator to run to an optimal pre-stop layer to enter a prediction waiting state when an obtained value meets a condition.

The application discloses a kind of fusion and compression method of multi-source trajectory information, it is related to data processing field.The application uses the thought of smoothing analysis, by adaptive calculation angle, speed error threshold, each positioning terminal trajectory is denoised in real time;Real-time calculation window time each trajectory data quality;According to the weight of each trajectory of trajectory data quality adaptive calculation, improved exponential weighted average algorithm is proposed, the weighted average value of longitude and latitude of multi-source trajectory point is calculated as the fused trajectory coordinates;The roughness of fused trajectory in sliding time window is calculated, and the sub-block size of GeoHash algorithm is dynamically adjusted by integrating trajectory roughness, to realize efficient real-time compression under the premise of maintaining high compression accuracy.The application can realize adaptive fusion and compression of multi-source trajectory by comprehensive data quality, with the advantages of reasonable fusion weight and high real-time compression efficiency.

The present disclosure provides a hardware resource allocation method, device and equipment of an electronic device, which preliminarily identifies hardware power consumption mutation by sampling the hardware power consumption data of the electronic device in real time and determining the fluctuation index corresponding to the current sampling data. Further, the exponential weighted value of the current sampling data is determined, and when the current exponential weighted value and the exponential weighted value when the last fluctuation index meets the set condition, the mutation is determined as an effective mutation. Furthermore, when the fluctuation index and the exponential weighted value meet the condition at the same time, the future hardware power consumption data is further predicted, and the hardware resource demand information of the electronic device in the next target period is determined in combination with the fluctuation index. The fluctuation of the hardware power consumption data can cover the user's fine-grained operation, and by predicting the future hardware resource demand information, the resources can be prepared in advance, avoiding the situation of insufficient or lagging resources during scene switching, and ensuring smooth user experience.

This filtering method comprises steps in which: a Kalman gain is calculated; a system calculates an estimated value on the basis of the Kalman gain; a predicted value and a measured value of an error covariance are compared and analyzed to calculate the error covariance; an exponentially weighted average and an exponentially weighted covariance for the estimated value are calculated; and the exponentially weighted average and the exponentially weighted covariance of the calculated estimated value are reflected in the subsequent Kalman gain calculation. Therefore, performance superior to that of a conventional Kalman filter can be achieved in non-Gaussian noise conditions.

The application discloses an intelligent evaluation system and method for a knowledge base question answering application, and relates to the technical fields of artificial intelligence, natural language processing and multi-agent collaborative system. The system comprises a data preprocessing module, a data synthesis module, a data screening module, a data scoring module and an adaptive weight adjustment module. The data preprocessing module performs data preprocessing on enterprise original documents and generates a data knowledge base. The data synthesis module repeatedly asks and answers each paragraph of the data knowledge base by using a large language model to generate a question and answer pair. The data screening module screens out high-quality samples through screening agent collaboration and a MACA framework mechanism. The data scoring module scores the question and answer pair through scoring agent collaboration and a MACA framework mechanism. The adaptive weight adjustment module dynamically updates the weight of each screening and scoring dimension by using an exponentially weighted moving average algorithm.

The application discloses a method for removing and realizing strong vibration interference in a force signal of a polishing robot, two filter algorithms are connected in series to form a double Kalman filter, a first filter realizes filtering of Gaussian white noise and high-frequency vibration spectrum peak group noise; a single parameter is introduced, a gradually time-varying noise variance is designed in an exponential weighting mode to describe the colored noise characteristics, and an improved Kalman filter is designed to form a second filter to realize filtering and elimination of the colored noise. The application realizes effective filtering and elimination of complex noise composed of Gaussian white noise, vibration noise and colored noise superimposed in force measurement information in robot force control polishing.

The invention discloses a Bernoulli filter-based single target detection and DOA tracking method and system, and relates to the field of target tracking. The method solves the problems that a traditional tracking algorithm after detection cannot jointly detect the target when the underwater target repeatedly enters under the conditions of low signal-to-noise ratio and small snapshot, and the tracking effect is unstable. The method comprises the steps of constructing a Bernoulli random finite set model, defining a single target state as a dynamic description target state or a dynamic description target state, predicting the target existence probability by using a Bernoulli Markov process, and generating a prediction state distribution particle set composed of new particles and survival particles through particle filtering. A hydrophone array signal is decomposed into a noise subspace observation model and a signal subspace observation model, a generalized likelihood function after exponential weighting is used for updating a target posteriori existence probability and a particle weight, and an equal-weight particle set is generated through resampling. And judging whether the target exists or not according to the single-target posterior existence probability, thereby realizing single-target DOA joint detection and tracking.

The invention discloses an in-situ monitoring data anomaly detection method and device based on trend decomposition and state modeling. The method comprises the following steps: preprocessing an ontology sequence and an environment sequence acquired in an in-situ monitoring area; decomposing the two types of sequences into a long-term trend term, a seasonal term and a residual term, normalizing, fragmenting and splicing into a fragment set; a fragment set is extracted in batches, and value, position and time coding and fusion are carried out on each component fragment; inputting each coding segment into a spatial state modeling module and an attention mechanism module, fusing results to obtain a predicted long-term trend, seasons and residual errors, and carrying out reverse normalization and splicing fusion to obtain a final prediction result; and processing a to-be-predicted ontology sequence and a corresponding environment sequence according to the previous steps, reasoning by using the trained model to obtain ontology prediction fragments, splicing the ontology prediction fragments into a continuous prediction sequence, calculating a prediction residual error, and performing bilateral exponentially weighted moving average anomaly detection. According to the invention, the accuracy and stability of anomaly detection can be effectively improved, and the timeliness and reliability of early warning are improved.

The invention discloses a trace element full-automatic detection system and method based on reinforcement learning. The method comprises the following steps: extracting target area and channel mark information by using image analysis, constructing a detection task atlas structural body, performing multi-round path simulation and return calculation in combination with a Monte Carlo tree structure, and generating a detection strategy path set. And detecting an action, selecting an action probability distribution tensor constructed based on an Exp3 algorithm, and realizing dynamic scheduling according to an exponential weighting weight. In the detection process, an actual result is collected, deviation calculation is carried out on the actual result and path simulation return, a feedback error vector is generated, a Monte Carlo tree structure and action probability distribution are updated, a strategy convergence state is judged, whether a strategy path set is reconstructed or not is controlled, and self-adaptive optimization of a detection strategy is achieved. According to the invention, self-adaptive scheduling and accurate control of the trace element detection process are realized.

The invention discloses a face velocity real-time monitoring method and system based on a ventilation cabinet, and relates to the field of anomaly detection, and the method comprises the steps: synchronously collecting a ventilation valve opening value, a pipeline internal pressure value and a face velocity value; step division is carried out according to the valve opening adjustment values, the adjustment values are calculated, hysteresis of pressure and wind speed response is considered, data are classified through a clustering algorithm, and step data under all the adjustment values are obtained; calculating an abnormal coefficient of each valve adjustment value based on time interval and deviation statistics of step data, and using an exponential weighting method according to the coefficient in combination with data recency and deviation degree; normalizing the abnormal coefficient according to the occurrence frequency of the opening degrees of the valves to obtain a final abnormal value of each opening degree; and judging whether the ventilation cabinet is normal or not by comparing the final abnormal value with a preset threshold value. The method effectively improves the monitoring accuracy, adapts to the accumulation characteristic of chronic faults, and ensures the safe operation of a ventilation system.

The invention relates to an RSSI-based indoor positioning method and device, a storage medium and electronic equipment, and relates to the technical field of indoor positioning, and the method comprises the steps: obtaining an RSSI value of at least one target signal received by a to-be-positioned target in an indoor region; according to the sequence of the RSSI values from large to small, selecting a preset number of signal transmitting nodes from the signal transmitting nodes, and determining the selected signal transmitting nodes as participating positioning nodes; according to the RSSI value of each participating positioning node, determining a target distance between the corresponding participating positioning node and the to-be-positioned target; determining an initial positioning coordinate corresponding to the to-be-positioned target according to the initial weighting coefficient and the coordinate information corresponding to each participating positioning node; and determining a final positioning coordinate corresponding to the target to be positioned through a preset weighted centroid positioning algorithm according to the exponential weighting coefficient and the coordinate information corresponding to each participating positioning node. The method has the effect of improving the indoor positioning precision.

The invention relates to the technical field of high-voltage relay life prediction, and discloses a high-voltage relay residual life prediction method based on an implicit non-linear scale transformation wiener process, and the method comprises the steps: carrying out the smoothing of the arcing time degradation data of a high-voltage relay through employing a moving average method based on the exponential weighting reinforcement recent influence; dividing different degradation stages of the high-voltage relay by using an important point clustering algorithm, extracting later-stage degradation data of arcing time, extracting a low-frequency intrinsic mode function component reflecting degradation characteristics of the high-voltage relay in the later-stage degradation data of the high-voltage relay through variational mode decomposition, and verifying feasibility of a wiener process; meanwhile, a degradation model is established for the later degradation stage of the high-voltage relay by using an implicit nonlinear scale transformation wiener process, model parameters are estimated, the influence of system noise is considered at the same time, and a drift coefficient is updated online through a Bayesian recursion method; and finally, predicting the residual life of the high-voltage relay based on the wiener process model in combination with the failure threshold value of the high-voltage relay, and evaluating the reliability.

The invention provides a traffic flow data intelligent monitoring method and system for a highway toll station, and belongs to the technical field of data monitoring. Comprising the following steps: preprocessing acquired traffic flow information into traffic flow data to be monitored, storing the traffic flow data to a corresponding database, and calculating current traffic flow data of each lane by adopting a time division method to determine fluctuation data of each lane; the method comprises the following steps: modeling a highway toll station and a surrounding environment based on a digital twin platform, and displaying fluctuation data of each lane; meanwhile, analyzing the fluctuation data of each lane by adopting an exponential weighted decline and skyline algorithm; and if the fluctuation data in any lane is abnormal, executing an alarm operation. According to the invention, accurate abnormity monitoring can be carried out for traffic flow characteristics with different date properties, potential peaks and abnormal fluctuations can be found in time, and a scientific decision basis is provided for lane management, traffic scheduling and safety guarantee of a highway toll station.

The invention provides a civil aviation operation-oriented airspace probability modeling and risk quantification method, which uses ADS-B data to construct layered space-time probability density, adopts an anisotropic kernel which is widened along the course and tightened in the transverse direction, and realizes short-time smoothing by using a time kernel. Inverse detection probability correction is introduced into the sample weight to reduce the coverage deviation. The reference density is subjected to multi-day exponential weighting generation according to hour data so as to describe day and night laws and workday characteristics. Under an information geometry framework, a relative entropy item, a Fisher information item, a capacity penalty item and a conflict kernel item are combined into a unified risk functional, and calculation of density evolution and a congestion dissipation direction is realized by a Wasserstein gradient flow. The system is composed of a data preprocessing module, a detection probability correction module, a layered anisotropic space-time kernel density estimation module, a reference density generation module, a risk functional and gradient flow module and an increment updating and index output module. The technology has unified modeling and clear interpretable and engineering landing characteristics, and can be used for situation analysis and collaborative decision support of air routes, sectors and terminal areas.

The application discloses a kind of based on multi-modal data fusion's intelligent early warning system and method of accompanying, belong to wisdom medical treatment and artificial intelligence technical field, this system includes multi-modal data acquisition and pre-processing module, dynamic weight fusion module, intelligent risk assessment module and multidisciplinary collaborative intervention module, through hardware synchronization and software interpolation, the time-space alignment of multi-source sensor data is realized, the dynamic weight fusion mechanism combining multi-head self-attention and evidence theory is used to process modal conflict, risk dynamic assessment is carried out using sliding window exponential weighting and LSTM time series modeling, and based on medical knowledge graph and improved hungarian algorithm, the intelligent scheduling and closed-loop feedback optimization of nursing resource are realized.

The invention relates to the field of artificial intelligence and natural language processing, and particularly provides a language model dynamic activation guide control method and system based on semantic feedback. The method comprises the following steps: S1, extracting intermediate layer activation in a language model generation process, and obtaining an interpretable semantic state through a sparse auto-encoder; s2, calculating the real-time difference between the current semantic state and the target semantic state; s3, dynamically calculating the current optimal guiding strength by adopting exponential weighting accumulation based on the historical semantic difference sequence; and S4, injecting the activation guide vector corresponding to the optimal guide intensity into the language model so as to adaptively control the output content of the language model in real time. According to the method, manual parameter adjustment is not needed, the problem of insufficient or excessive control in static guidance can be effectively avoided, the semantic consistency and quality of the generated text are remarkably improved, and the method can be widely applied to multiple tasks such as text generation, style control and safety control.

The invention discloses an input and output monitoring system and method for a vibration active control system, and solves the problems that the existing monitoring hardware is complex, the precision is low, the resource occupation is large, and the control performance is influenced. The system comprises an error signal monitoring module, a controller output signal monitoring module and a state monitoring module, a hardware architecture is built by means of a digital processor (DSP), and an error sensor and a controller are respectively connected with corresponding interfaces of the DSP and externally connected with display and storage equipment. The error signal monitoring module comprises a narrowband filtering unit and an exponential weighted accumulation and correction unit, the controller output signal monitoring module comprises an exponential weighted accumulation and correction unit, and the monitoring method is executed according to the steps of initialization, error signal monitoring, output signal monitoring and state observation. According to the invention, extra signal acquisition hardware is not needed, narrow-band filtering and an exponential weighting algorithm are combined, the monitoring precision and the control performance are improved, and the method is suitable for a vibration active control scene.

The application discloses a kind of distributed power grid-connected control method and device, by accurately time alignment with power supply side power data and grid side voltage frequency data and combining static and dynamic phase compensation eliminates collection error;Power fluctuation cumulative grading and exponential weighted dynamic reference correction are used to evaluate power deviation under different severity, and additional credibility correction factor improves the reliability of deviation evaluation;Introduce bidirectional power flow link identification and transmission link loss nonlinear accumulation technology combined with load rate correction factor to realize accurate compensation of power regulation amount;By steady-state holding domain shrinkage analysis and disturbance propagation domain expansion analysis, the disturbance influence range is defined to construct adaptive response parameter set with overlapping transition characteristics;Fusion nonlinear grading gain strategy of linear regulation zone and limiting amplitude regulation zone is based on gain gradient mutation position Adaptive setting of grading regulation threshold value outputs grid-connected control instruction, improve the reliability and power regulation accuracy of distributed power grid-connected operation.

The invention provides a cold and hot data distribution management method and device for a nonvolatile storage device. Aiming at the mixed storage requirement of real-time data and historical data in an industrial control scene, a dynamic shunting method based on logic space popularity evaluation is provided. A 512GB SSD user address space is divided into 64000 logic spaces, an exponential weighting algorithm is adopted to collect operation frequency data in real time, an access sequence Order array is generated in combination with a DRAM historical double linked list, and a comprehensive heat value is calculated. And dynamically generating a threshold value based on the quantile coefficient, distributing the logic space with the heat value greater than the threshold value to the low-wear flash memory block with the erasure times less than or equal to 5000, and distributing the logic space with the heat value less than the threshold value to the high-wear block. According to the scheme, the storage life is greatly prolonged, the real-time instruction response delay is reduced, the hybrid load characteristic of industrial control high-frequency real-time and low-frequency archiving is adapted, and the balance between data access efficiency and storage reliability is ensured.

The application provides a non-volatile storage device cold and hot data shunt management method and device. In view of the mixed storage demand of real-time data and historical data in the industrial control scene, a dynamic shunt method based on logical space heat evaluation is proposed. By dividing the 512GB SSD user address space into 64000 logical spaces, using the exponential weighting algorithm to collect operation frequency data in real time, combining the DRAM history bidirectional list to generate the access order array, and calculating the comprehensive heat value. Based on the quantile coefficient, the threshold value is dynamically generated, the logical space with a heat value greater than the threshold value is allocated to the low-wear flash block with an erase number ≤5000, and the logical space with a heat value less than the threshold value is allocated to the high-wear block. The scheme realizes the substantial prolongation of the storage life, the reduction of the real-time instruction response delay, the adaptation to the mixed load characteristics of the high-frequency real-time and low-frequency archiving of industrial control, and ensures the balance between the data access efficiency and the storage reliability.

Novel tools and techniques are provided for implementing exponentially weighted scoring of calculated resource availability for automated network element selection, augmentation, and service delivery. After receiving a request for network services and measurement data associated with a plurality of network elements capable of providing the requested network services, a computing system may calculate a weighted score for each of a plurality of resources that is available on each network element. Each weighted score for a corresponding resource may be based on an exponential decay function of a difference between a limit value corresponding to an allocatable amount of the corresponding resource and a measured usage value based on the measurement data. For each network element, the computing system may calculate an availability score based on the weighted scores, and may output either the availability score and / or a resource allocation decision for that network element.

The invention relates to the technical field of stage safety monitoring, and discloses a stage safety monitoring method based on pressure induction, which comprises the following steps: constructing a dynamic pressure baseline and a variance baseline through the combination of a sliding window and exponential weighting; automatically generating an adaptive threshold based on a preset false alarm rate and a normal distribution inverse function; calculating pressure deviation in real time and judging abnormity; performing event type classification by using the multi-dimensional differential pressure vector and a mahalanobis distance in historical normal distribution; weighting the forward pressure difference to solve the gravity center so as to estimate the event position; sensor fault diagnosis and elimination are realized based on continuous ultralimit normalized residual errors; the method does not need to depend on empirical weight and complex training, and has self-adaption, accurate positioning and fault self-checking capabilities.