297 results about "Weight prediction" patented technology

Embodiments of this disclosure can provide a system and method for training a perception model to perform an autonomous driving task. During operation, the system can obtain labeled training data comprising images captured by multiple cameras mounted at different locations on a vehicle, and the perception model can generate, in parallel, a prediction output associated with the task and a confidence score based on the labeled training data. The confidence score can indicate a level of uncertainty associated with the prediction output. The system can generate an uncertainty-weighted prediction based on ground truth indicated by the labeled training data, the prediction output, and the confidence score; compute a loss function based on the uncertainty-weighted prediction; and update the perception model based on the loss function.

The invention relates to a sea cucumber growth character recognition and measurement method based on machine vision and a measurement system thereof, and belongs to the field of image processing, and the method comprises the following steps: S1, image acquisition and preprocessing; s2, instance segmentation and morphological feature extraction; s3, measuring the length and width of the sea cucumber; s4, pixel-to-actual size conversion is carried out; s5, constructing a body weight prediction model; and S6, outputting a result. The method has the advantages that the sea cucumber image is obtained by using the high-definition image acquisition technology, the contour information of the sea cucumber is accurately extracted through the instance segmentation algorithm, and the problems of changeable and irregular sea cucumber shapes and the like are effectively solved by combining the optimized image processing technology, so that the measurement precision is remarkably improved. Meanwhile, a machine learning regression model is introduced, the weight of the sea cucumber is predicted based on various morphological characteristics, and the dimensionality and the utilization value of measured data are further enriched.

The embodiment of the invention discloses a dynamic computing power scheduling method and device based on deep learning and a medium, belongs to the technical field of cloud computing, and solves the problem that cloud computing resources are seriously wasted due to the fact that a cloud computing scheduling method in the prior art is prone to unbalanced resource allocation. Comprising the following steps: preprocessing acquired cloud computing data to generate a structured feature vector; wherein the cloud computing data at least comprises cloud node state data, computing task attributes and environment data; through a multi-head attention mechanism, task resource space-time correlation features corresponding to the structured feature vectors are extracted; the task resource space-time correlation features are input into a weight dynamic prediction module and a resource matching degree scoring module at the same time so as to carry out parallel processing of weight prediction and matching degree scoring calculation; and solving a multi-objective optimization function corresponding to computing power scheduling based on a parallel processing result, and generating a computing power scheduling instruction based on a solving result.

The invention relates to the technical field of data processing, in particular to a legal case relation graph dynamic updating method based on a neural network. Comprising the steps that original legal case data are collected and preprocessed to obtain target legal data; entities in the target legal data are extracted to serve as nodes, node sub-graphs are obtained through a machine learning model, weight parameters corresponding to the nodes are selected from weight parameter sub-graphs, weights of node edges are obtained based on the weight parameters and a weight prediction model, and a first graph is formed; acquiring newly added legal data, extracting new nodes, acquiring newly added sub-graphs through a machine learning model, and fusing the newly added sub-graphs with the first graph to obtain a second graph; and generating a search vector according to key weight parameters of adjacent nodes and edges in the second map, and embedding the search vector into the second map. According to the method, the problems that the legal case relationship is complex and cannot be dynamically updated are solved, the legal case relationship graph can be dynamically updated, and the accuracy and timeliness of the graph are improved.

The invention relates to the technical field of voice signals, in particular to a two-step mixed sound source separation and de-reverberation method, which comprises the following steps of: firstly, carrying out separation network training by taking different types of signals as training targets in various separation networks for separating attention and the like; the invention provides an improved de-reverberation method based on a time convolution network-weight prediction error, multiple improvement strategies such as taking a scale invariance signal-to-noise interference ratio as a network loss function, adopting a transposition mechanism for an input signal and a mechanism for additionally adding a residual value to a network unit are used, and finally, the de-reverberation method based on the time convolution network-weight prediction error is obtained. And cascading the separation attention network with the best separation noise reduction effect with the time convolution-transpose-residual error-WPE network. According to the invention, the problem that the separation effect of the mixed audio signal with reverberation and noise signals is not good under the actual sound field condition is solved, compared with a single one-step or two-step existing separation, de-mixing and noise reduction network, the method is significantly improved, and the quality of the separated voice can be improved for later recognition and discrimination.

The invention relates to a multi-level sentiment classification method for network public opinions. According to the method, social media data are acquired and processed in real time through a distributed message queue, online clustering is performed by using a Streaming K-means algorithm, and an initial topic set is generated. A topic state space is constructed through a topic state analysis model, a proper classification level is selected by using a Q-learning algorithm, and an information entropy data set is constructed through dependency path analysis and information entropy calculation. And finally, generating a multi-level sentiment classification result through the level weight prediction model and the sentiment classification model, and using the multi-level sentiment classification result for network public opinion monitoring. The method can adapt to changes of network public opinions in real time, and the accuracy and timeliness of sentiment classification are improved.

The invention relates to a resource elastic scaling decision-making method, system and device and a medium. The method comprises the following steps: collecting real-time operation data of a security service node, and performing multi-dimensional security index analysis according to the real-time operation data to obtain a portrait data packet; predicting the security service weight value to obtain a prediction result, performing dynamic error compensation on the prediction result to generate a corrected weight prediction value, and generating a control instruction based on the corrected weight prediction value and the active session state; and when the instruction is a migration instruction, analyzing a session state snapshot of the instruction, calling a preset kernel state locking function to lock a memory session block of a source node, obtaining incremental state change data to generate a migration snapshot packet, and performing block verification injection operation on a target node. According to the method, by integrating multi-dimensional safety index analysis, prediction error compensation and stateful transition verification mechanisms, the accuracy and response efficiency of resource elastic scaling decision making are improved, and the continuity of stateful service transition and the consistency of safety strategies are enhanced.

The invention discloses an ultrahigh-definition panoramic image adaptive HDR fusion method based on data driving, and relates to the technical field of digital image processing, and the method comprises the steps: carrying out the coarse alignment and high dynamic range fusion of a low-resolution line graph sequence, and generating a low-resolution HDR panoramic image; carrying out sharpening processing on the low-resolution HDR panoramic image to obtain a low-resolution sharpened HDR image, and meanwhile, obtaining a multi-scale residual spectrum generated by sharpening; performing up-sampling on the multi-scale residual spectrum, and performing small-sample element learning fine tuning on the weight prediction network in combination with the low-resolution sharpened HDR graph to obtain a fine-tuned weight prediction network; performing weight prediction and layer-by-layer fusion on the registered high-resolution tile sequence on a multi-scale pyramid based on a fine tuning weight prediction network model; according to the method, the sharpening processing is implemented on the low-resolution HDR panorama, and the multi-scale residual spectrum is extracted, so that the detail sensitivity and adaptability in a complex scene are improved.

The invention discloses an AI and algorithm fused intelligent geological layering system and method for geotechnical engineering investigation, and the method comprises the steps: data importing and preprocessing, data verification and gross error elimination, automatic classification of related / non-related variables, cleaning and iterative updating of layer mean values and boundaries, and hierarchical division. According to the method, the data is automatically cleaned, the layer group is verified, the main layer / sub-layer / sub-layer / interbed is divided, the layering accuracy and efficiency are improved, the artificial intelligence technology is applied, and the layering efficiency is improved. Technicians are replaced to complete the heavy work flow of drawing grass sections, manually layering, inputting soil layer information into a computer, manually judging the rationality of in-situ test, geotechnical and rock test data and layering, modifying sketches, secondarily proofreading the rationality of results and completing division, and the exploration efficiency is improved.

The invention provides a multi-modal pig weight measurement method and system based on attention fusion, and the method comprises the steps: S1, data collection: employing a depth camera to synchronously obtain an RGB image and a depth image of a pig, and converting the depth image into point cloud data; s2, multi-modal feature extraction: extracting 2D features from the RGB image, and extracting 3D features from the point cloud data; s3, feature fusion: performing cross-modal feature fusion based on an attention mechanism on the extracted 2D features and 3D features to obtain attention fusion features; and S4, MLP regression prediction: inputting the attention fusion features into an MLP weight regression prediction model, and predicting the weight of the pig through the MLP weight regression prediction model. According to the method, the 2D features and the 3D features are fused, so that the adaptability of the system in a complex scene is enhanced, and the accuracy of weight prediction can be greatly improved.

Systems, methods, and apparatus for video processing are described that include weighted prediction for video blocks. An example method includes: performing conversion between a current slice of a current picture of a video and a bitstream of the video, wherein the bitstream conforms to a format rule, and wherein the format rule specifies the presence of a general constraint information syntax structure that includes one or more constraint flags indicating that constraints on explicit weighted prediction are enabled for slices of a set of pictures.

The invention discloses a logistics return transportation capacity intelligent matching system and method based on journey prediction, and the method comprises the steps: carrying out the transportation capacity departure time window prediction, journey time consumption prediction, space correction prediction, sudden factor correction prediction and bearable weight prediction of the multi-source heterogeneous data of each vehicle in the current collection period; obtaining a transport capacity departure time window, an optimal return path, return time consumption and an estimated loadable weight of the target vehicle; and generating a transport capacity label based on the transport capacity departure time window, the optimal return path, the return time consumption and the estimated bearable weight of the target vehicle, performing multi-dimensional adaptation on the transport capacity label and the order information to obtain a comprehensive score of the transport capacity label, and performing order matching and state updating operation of the target vehicle based on the comprehensive score of the transport capacity label. Accurate pre-judgment of the transport capacity state is achieved, and the invalid matching rate is greatly reduced.

The invention belongs to the field of robot control, and provides a multi-view feature fusion operable part semantic segmentation method and system, and the method comprises the steps: obtaining the point cloud data of an operable part, and generating a corresponding multi-view image; carrying out target detection, extracting features for each view angle, and obtaining a two-dimensional bounding box and a semantic tag; based on the extracted two-dimensional bounding box, processing by using SAM to obtain a foreground mask; on the basis of the extracted two-dimensional bounding box, the relevance of the same semantic target under different visual angles is captured on the global scale by using the constructed global visual angle interaction module, and the feature consistency is enhanced; and processing the fused bounding box features obtained by the global view angle interaction module by using a weight prediction network, predicting the response weight of each bounding box at each super point, combining the obtained foreground mask and the predicted response weight, and back-projecting to a 3D point cloud space through view point information to obtain a final 3D semantic segmentation result. According to the invention, the segmentation accuracy is improved.

The invention discloses a method for realizing foreground multi-modal detection based on adaptive dynamic weight fusion, which comprises the steps of multi-modal data acquisition, multi-modal data preprocessing, multi-modal feature extraction, multi-modal feature fusion, foreground detection and prediction result post-processing. The multi-modal feature fusion step comprises the following steps: a) respectively inputting the extracted RGB features and IR features into a weight prediction sub-network, wherein the sub-network generates a spatial weight map for each modal; each pixel value in the weight map represents the local confidence degree of the mode for foreground discrimination at the corresponding spatial position; b) normalizing the spatial weight maps corresponding to the modalities to enable the sum of the weights of the modalities at the same spatial position to be 1; and c) multiplying the normalized spatial weight map by the corresponding modal features element by element, and adding the modal weighted feature maps element by element to obtain a final comprehensive feature map for foreground detection. According to the method, the foreground detection precision and robustness are remarkably improved.

The invention discloses a future pork pig group weight estimation method and system based on multi-model fusion, and the method comprises the steps: obtaining a historical structural data set of a pork pig breeding complete period, and employing a multi-modal fusion evaluation method of field priori knowledge weighted evaluation, data-driven feature importance calculation and an automatic code analysis tool. Extracting a key factor set influencing the weight of the pork pig, then based on the historical structured data set and the key factor set, respectively constructing an expert rule reasoning model, a data-driven prediction model and a code generation type dynamic model to predict the weight of the pork pig, inputting each model based on real-time data, and outputting a prediction value; weights are dynamically distributed according to historical errors, results are fused, abnormal values are removed in combination with discrete degree detection, and a final fusion prediction value is generated. The technical problem of improving the weight estimation accuracy of the pork pig group in the prior art is solved.

The invention discloses a bicubic interpolation image super-resolution reconstruction method based on dynamic weight prediction, which relates to the technical field of image super-resolution, and is characterized in that a lightweight convolutional neural network is trained through an improved bicubic interpolation algorithm based on dynamic weight parameter adjustment to obtain a dynamic super-resolution model to realize image super-resolution reconstruction; according to the dynamic super-resolution model, a bicubic interpolation algorithm is improved by establishing a dynamic weight adjustment mechanism based on local contrast, so that an optimized prediction global weight matrix is obtained, contribution of a high-contrast direction is enhanced in an original low-resolution marginal region, noise is suppressed in a flat region, and the dynamic super-resolution model is obtained. And keeping the balance smoothness and details of the texture region. The invention provides a bicubic interpolation image super-division reconstruction method based on dynamic weight prediction, which converts a high-dimensional pixel generation task into a low-dimensional weight optimization problem and remarkably compresses the complexity of a model.

A method for generating a standard growth curve for chicken flock breeding comprises the following specific steps that a, the same kind of chicken flock is divided into N groups to be bred, and it is guaranteed that all chickens in each group of chicken flock are born on the same day; b, recording the daily average weight of each group of chicken flocks; c, taking the daily average weight of all groups of chicken flocks as a training set, learning in a learning model, averaging, and finally generating a daily age average weight standard growth curve of the same type of chicken flocks by means of a Gompertz fitting model; and d, changing any condition of variety, gender, region, henhouse control mode, climate and breeding mode to form a new type of chicken flocks, and repeating the steps b and c to obtain daily age average weight standard growth curves of different types of chicken flocks. The daily age average weight standard growth curve obtained by the method is more consistent with the actual weight of the chicken flocks, the average weight prediction of the chicken flocks is more scientific and accurate, and meanwhile, the problem of physical health of the chickens caused by a mode of catching the chickens by sampling is avoided.

The invention relates to the field of fruit quality nondestructive testing, in particular to a winter peach cold storage shelf life classification and quality nondestructive testing method based on spectrum-image feature fusion and stacked model fusion, and the method is used for accurately classifying the winter peach cold storage shelf life and predicting the key quality of the winter peach cold storage shelf life based on hyperspectral imaging and deep learning technologies. The method comprises the following steps: acquiring a hyperspectral image in a wave band range of 400-1000nm, and extracting a region of interest through mask processing; phenotype data of the samples are measured and classified, and abnormal samples are removed; through spectrum preprocessing and characteristic wavelength screening, effective pixel size and area parameters are extracted, and a multi-task integration model is constructed. The model not only realizes 100% accurate classification of the shelf life of the winter peaches, but also predicts the sugar degree (R2 = 0.8451) and the hardness (R2 = 0.8798), and supports a pseudo-color visual distribution diagram and fruit diameter and weight prediction. The method is high in precision, good in stability and high in automation degree, and effective technical support is provided for quality management and market circulation after agricultural products are harvested.

The invention provides a large-scale low-orbit satellite network service volume prediction method based on regional mapping, which comprises the following steps of: dividing the time of each day into a plurality of fixed-length time periods, and dividing the earth surface into a plurality of earth surface service regions; through a bilinear regression weighted prediction model and historical business volume data, obtaining all predicted business volumes between the first surface business area and the second surface business area in a certain time period of a certain day; in a certain time period of the certain day, acquiring a first service volume sharing weight of a first satellite to a first earth surface service area, and acquiring a second service volume sharing weight of a second satellite to a second earth surface service area; and according to all the predicted service volumes, the first service volume sharing weight and the second service volume sharing weight, calculating and acquiring predicted service volumes of the first surface service area and the second surface service area covered by the first satellite and the second satellite in the same time period. The method has the advantages of being simple, high in efficiency and accurate in prediction.

The invention relates to the technical field of deep learning, and provides a mammary gland blood oxygen dynamic imaging method and system based on multispectral fusion, and the method comprises the steps: irradiating mammary gland tissues, and collecting mammary gland tissue reflected light image sequences under different wavelengths to obtain multispectral time sequence data; constructing an adaptive weight prediction network model, and dynamically predicting the fusion weight of each spectral wavelength at different moments in the future according to the blood oxygen change intensity and historical time sequence information at the current moment; performing multi-resolution time sequence modeling on the multi-spectral time sequence data, respectively extracting short-term, medium-term and long-term time sequence features, and performing weighted fusion on the features of different spectral wavelengths through a fusion weight to obtain fusion feature data; and performing inversion processing on the fused feature data, and calculating oxyhemoglobin saturation distribution of a continuous time sequence in real time to obtain a dynamic blood oxygen image sequence. According to the invention, the blood oxygen detection precision and the depth resolution are improved, and high-precision dynamic imaging of the blood oxygen saturation of the breast tissue is realized.

The invention discloses a tough city evaluation method based on climate and biodiversity coupling and application. The evaluation method comprises the following steps: S1, determining a biodiversity index of a city to be analyzed; s2, based on a weight prediction model, determining a climate partition weight coefficient of the biodiversity index in a future time period; s3, an index evaluation value BRI is obtained through calculation by means of the biodiversity index and the climate partition weight coefficient, and the larger the index evaluation value BRI is, the higher the city toughness is; according to the method, the climatic factors and the biodiversity are subjected to coupling analysis, the weight prediction model is established to predict the biodiversity index in the future time period, the toughness of the urban biodiversity can be quantified, the limitation of static evaluation of an original evaluation method is solved, accurate evaluation of climatic partition adaptation is realized, and the evaluation efficiency is improved. A tough city evaluation system with better ecological adaptability is constructed, and a scientific tool is provided for global cities to cope with climate changes.

The present invention relates to the technical field of engine condition monitoring, and particularly relates to an engine performance prediction method using sample adaptive weighting, which comprises the following steps: establishing a first data set and a second data set, wherein the first data set is the already-operated data set of the current engine, and the second data set includes the operation data sets of N other engines of the same model as the current engine; clustering the first data set and the second data set according to the flight envelope distribution to obtain M clustering centers; establishing sub-models such that each of the M clustering centers includes N sub-models; initializing a prediction weight vector W for each of the clustering centers, and performing weighted averaging on the N sub-models included therein to construct M linear weighted prediction models, which are used to predict engine performance; and optimizing the prediction weight vector W of the clustering centers. This method gives full play to data diversity and meets the requirements for accurate performance prediction of aero-engines.

The invention discloses a chicken body size and weight automatic estimation method based on multi-view image fusion. The method comprises the steps of multi-view image acquisition and preprocessing, and body size estimation key point detection. Body weight estimation; visual angle fusion and parameter calculation; calculating a pixel-real distance conversion factor F through the solid circular array calibration plate, and converting a pixel distance into a real entity ruler parameter; and establishing a body size-body weight incidence matrix, inputting the body size parameter and the body weight prediction value into a full connection layer, and outputting the optimized body weight prediction value. Compared with traditional manual measurement, the method has the advantages that the measurement time consumption of a single chicken is greatly shortened, and rapid and automatic detection is realized; equipment such as a high-cost depth camera does not need to be used, hardware investment is remarkably reduced, meanwhile, labor dependence is reduced, labor cost is effectively controlled, and the batch monitoring requirement of a large-scale farm is highly met.

The invention provides an AI-based multi-user interactive language learning system and method, and relates to the technical field of interactive language learning. Real-time structured analysis of a multi-user mixed voice stream is realized through a voiceprint separation technology, a grammar error density spectrum is constructed to quantify user grammar error distribution characteristics, and a culture conflict intensity field is established; cross-cultural dialogue conflict intensity is dynamically calibrated, a semantic transition trajectory is generated, a logic fault evolution law is accurately captured, and a traditional single-dimensional static learning mode is broken through; a language adaptation index is dynamically generated on the basis of a weight prediction model, collaborative modulation of a grammar reconstruction task package, a culture tuning script and a semantic bridging task is achieved, culture conflict mediation deeply conforms to real-time conflict intensity, and dialogue fault features are accurately matched through semantic logic repair; through a three-channel task distribution mechanism of directional insertion, multicast broadcast and random allocation, efficient collaboration of personalized grammar training, immersive culture drill and distributed logic repair is ensured.

The invention relates to the technical field of power systems and intelligent power grids, and discloses an automatic monitoring method and system for a booster station of a wind power plant, and the method comprises the steps: collecting the operation data of a main transformer, a circuit breaker and a bus through arranging edge equipment in a power grid system of the booster station of the wind power plant, and constructing a power topological graph; constructing a graph neural network anomaly detection model, and outputting a node anomaly probability and an edge anomaly probability through node embedding aggregation and edge weight prediction; determining the importance score of each node based on the node influence factor of the topological graph; sorting all the nodes from high to low according to the importance scores, and selecting the m nodes before sorting to participate in each round of federated learning until the loss function of the neural network anomaly detection model is smaller than a set threshold value; and monitoring the operation state of the booster station of the wind power plant in real time by using a graph neural network anomaly detection model, and triggering a multi-stage alarm signal. And the dynamic adaptability and monitoring precision of wind power plant booster station monitoring are improved.

The invention relates to a unit excitation system fan energy-saving control method based on multi-parameter dynamic adjustment. The method comprises the following steps: acquiring joint regulation and control data of a target fan; inputting the combined regulation and control data into a trained dynamic weight prediction model, wherein the dynamic weight prediction model outputs a dynamic weight corresponding to each type of data based on the relevance between each type of data in the combined regulation and control data and the fan energy-saving effect of the excitation system of the target fan; based on the dynamic weight corresponding to each type of data in the joint regulation and control data, determining a comprehensive regulation coefficient; based on a preset first mapping relation table, determining a target fan rotating speed matched with the comprehensive adjustment coefficient; and controlling the target fan to operate according to the target fan rotating speed. According to the scheme, the fan energy consumption is effectively reduced.

The embodiment of the invention discloses a logistics package weight information abnormity identification method and electronic equipment. The method comprises the steps of establishing a logistics package weight database of a commodity minimum stock unit SKU dimension according to multiple pieces of forward logistics data; using the data in the database to train a commodity weight prediction model; receiving weight information of a returned goods package uploaded by a logistics service party in the process of providing a returned goods collection service, and determining associated returned goods order information and returned goods SKU information associated with a returned goods order; predicting logistics package reasonable weight interval information of the returned commodity SKU through the commodity weight prediction model; and judging whether the weight information of the returned goods package returned by the logistics service party is within the reasonable weight interval range or not, and performing abnormity judgment. According to the embodiment of the invention, the credibility of the returned goods weight data is improved, and the platform risk is reduced.

The invention relates to the technical field of semiconductor manufacturing, in particular to a semiconductor machine control method and device, a medium and a product, and the method comprises the steps: training an initial weight prediction model according to training time sequence data until a weight prediction error is not greater than a preset target value, and obtaining a target weight prediction model; inputting the test time sequence data into the target weight prediction model, and predicting a target weight; the test time sequence data comprises process parameter time sequence data, machine state parameter time sequence data and process measurement result time sequence data; according to the predicted target weight and the actual feedback data and historical feedback data of the current batch of wafers, obtaining process parameter prediction data of the current batch of wafers; and controlling the semiconductor machine to execute the target technological process on the current batch of wafers according to the technological parameter prediction data. And at least according to the dynamic change of the actual working condition of the machine, the process parameters of the machine can be dynamically tracked and predicted in real time.

The invention provides a crop moisture utilization rate estimation method and system based on unmanned aerial vehicle remote sensing data, and the method comprises the steps: obtaining meteorological data of a crop planting region, and laser radar point cloud data, multispectral data and thermal imaging data of a target crop group in the crop planting region; according to the multispectral data, the thermal imaging data and the meteorological data, obtaining the corresponding target evapotranspiration of the target crop group in each day; inputting the laser radar point cloud data, the multispectral data and the thermal imaging data into the crop population overground part dry matter accumulation amount prediction model to obtain overground part dry matter weight prediction data; according to the overground part dry matter weight prediction data corresponding to different prediction moments, performing fitting to obtain a target dry matter accumulation amount corresponding to the target crop group in each day; and obtaining the crop water utilization rate of the target crop group according to the target evapotranspiration and the target dry matter accumulation amount. The precision and efficiency of crop water utilization rate estimation are improved.

The invention provides a heat pump ring fin difference control method and system based on dew-point temperature prediction, and the method comprises the steps: calculating the current dew-point temperature based on the air temperature and the relative humidity, and obtaining a future dew-point temperature sequence through linear weighting prediction based on historical dew-point data; generating a candidate control action set based on the current dew point temperature, the future dew point temperature sequence, the current fin temperature, the current opening degree of the electronic expansion valve, the rotating speed of the fan and the upper limit of the adjustment stride; screening candidate action subsets through an evaluation function; for each candidate action, using a semi-analytical system response simulator to predict a future fin temperature sequence, and calculating a difference sequence between the fin temperature and the dew point temperature based on the future fin temperature sequence; carrying out weighted calculation on a risk score for the difference value sequence of each action, and screening an action set based on a risk score threshold value; and an objective function is constructed, the action with the minimum objective function value is selected as the optimal control action, and the optimal control action is issued to the electronic expansion valve and the fan driver through PWM signals.