83results about How to "Improve algorithm performance" patented technology

The invention discloses a movement capturing method based on multiple binocular stereo vision. A movement video collecting device is constructed, and human movement video sequences from different orientations are collected by the movement video collecting device. Multiocular movement video sequences shot by a plurality of cameras are calibrated. Marked points matching and tracking of each binocular tracker is finished. Data fusion of three-dimensional tracking result of multiple binocular trackers is completed. The three-dimensional movement information of the marked points acquired by a multiocular fusion device is fed back to the binocular tracker to consummate binocular tracking. On the basis of binocular three-dimensional tracking realized by binocular vision, the invention fuses multiple groups of binocular three-dimensional movement data, resolves parameter acquiring problem of three-dimensional position, tracking, track fusion and the like for a plurality of marked points, increases number of traceable market points and enables the tracking effect to be comparable with three-dimensional movement acquiring device employing multi-infrared cameras for collecting.

A method for accurate and rapid automated detection of atrial fibrillation (AF), sinus rhythm (SF), and atrial flutter (AFL) is disclosed, which allows distinguishing of these cardiac signals with lowered risk of errors by implanted pacemakers and like devices. The method includes training episodes of intra-cardiac signals (called the closed data set CDS) to evaluate five feature parameters with a discriminator classifying the signal into AF, AFL or sinus rhythm (SR). Comparison with the independent decisions of experienced physicians for each episode reveals specificity, accuracy and sensitivity of greater than 97%. Each episode is a window of intracardiac signal of interval 1-2 seconds with the discriminator providing results in less than 0.25 s. In another aspect, the method is resistant to the presence of noise in the data. In yet another aspect, more feature parameters may be used in alternative implementations including for detecting signals other than AF, AFL & SR.

A real-time stereo camera disparity estimation device comprises input means arranged to input measured data corresponding to rows of left and right images; a plurality of on-chip memories arranged to buffer the input measured data; a vertical rotator hardware module configured to align the rows of left and right images in a same column; a reconfigurable data allocation hardware module; a reconfigurable computation of metrics hardware module; and an adaptive disparity selection hardware module configured to select disparity values with the minimum matching costs.

The invention relates to an augmented reality based mobile platform three-dimensional biomolecule display system and an augmented reality based mobile platform three-dimensional biomolecule display method. The identification of the traditional paper print biomolecule two-dimensional picture and the three-dimensional real-time display of the corresponding molecule are realized on a mobile platform. The image feature point detection and matching method is improved, so that the display is collaboratively completed by a mobile client and a cloud server, the flexibility is improved, and the client resources are reduced. Three-dimensional molecular modeling data is derived from a PDB (Protein Data Bank), so that the accuracy and the authority are ensured. The augmented reality technology is expanded, a typical application is provided in biochemistry and molecular biology education learning aspects, and the technical means and the framework can be applied to any education and teaching fields; because of the development and popularization of a mobile terminal, and the portable characteristic of the mobile terminal, the system can be used by users at any time and any place, so that the teaching efficiency and the interest in learning are greatly improved.

The invention relates to the field of human body motion recognition, and provides a human body motion recognition method based on multi-sensor data fusion. The method comprises the following steps: human body motion data are collected by using N inertial sensor nodes respectively fixed at different parts of the human body; sliding window segmentation technology is used to segment the human motiondata collected from each sensor node to obtain multiple motion data segments of each sensor node; feature extraction is carried out on the motion data segment of each sensor node to obtain the corresponding feature vector; an RLDA algorithm is used to reduce the dimension of eigenvectors of each sensor node; the reduced dimension eigenvector of each sensor node is used as training data for parameter training and modeling, and the corresponding hierarchical fusion model is obtained; using the hierarchical fusion model, human motion recognition is carried out. The invention can effectively overcome the drawbacks of the single classifier in the identification process, and can effectively improve the human motion identification accuracy.

The present invention relates to an image blur processing method and device, a storage medium and an electronic device, which are used for solving the technical problem that the image blur processingeffect is not ideal in the prior art. The method comprises the following steps of: selecting a reference pixel point of a target pixel point on an image to be processed; calculating a pixel differencevalue between each reference pixel point and a target pixel point; determining a weight value of each reference pixel point at least according to a preset correspondence between a pixel difference value and a weight value; calculating a blur value of a target pixel point according to the weight value of each reference pixel point; blurring the target pixel according to the blurred value.

The invention discloses a child emotion recognition method based on a multi-attention mechanism long-short-time memory network. the method comprises: detecting an end point of a test set speech and divides the speech into frames to extract time sequence related features, the processing algorithm of extracting temporal correlation features with different lengths based on long-short time memory network; the strategy of combining the attention mechanism with the depth of time sequence is introduced into the forgetting gate, input gate and final output of the long and short time memory network. Finally, the samples to be tested are input into the improved long-short time memory network in the training process, the emotional information can be distinguished distinctly, By introducing the depthof attention mechanism combined with timing into the forgetting gate, the output gate and the final output of the long-short time memory network, the invention not only greatly reduces the parameter quantity, but also improves the algorithm performance, increases the flexibility of the method design, and has high identification efficiency and good application prospect.

According to the method in the field of medical image processing, an efficient medical image marking and learning system is realized. According to the system, an unsupervised comparative learning pre-training module introduces an unsupervised comparative learning method in a pre-training process based on an MoCo algorithm; the training module based on active learning and comparative learning obtains a pre-trained model and an actual target task data set; and further adjusts the model to adapt to the actual demand by discovering the most valuable sample in the unlabeled samples to obtain the label and providing the difficult case mining of higher weight for the sample classified by mistake and introducing the supervised contrast learning method of labeled data; and a manual annotation and model training interaction control module provides an annotation interaction interface and a model training control module. Through the system of the architecture, a universal training framework is realized.

The invention discloses an optimized extreme learning machine binary classification method based on improved active set algorithms. The method comprises the following steps of: firstly, providing a training sample set, and determining an optimized solution problem; secondly, calculating a decreasing direction dk according to a BAS active set algorithm, and finding a maximum searching step size without violating constraint conditions along the decrease direction dk; thirdly, setting a temporary iteration step size according to an EAS active set algorithm, and continuously finding an optimal step size; and fourthly, utilizing a prediction assignment method to reduce the calculation cost of iterating a working set variable to the optimal value. An optimized extreme learning machine is adopted to carry out classification on unknown samples.

A system and method for detecting spectrum holes in a region is disclosed. The system comprises a requesting module, a detecting module and an aggregation module. The requesting module retrieves channel availability data for a region. The channel availability data describes potential availability of a channel. The detecting module obtains a set of values for the channel by spectrum sensing at a set of locations in the region based at least in part on the potential availability of the channel. The aggregation module aggregates the set of values for the channel over the set of locations in the region to obtain a first value for the channel and determines the availability of the channel based at least in part on the first value for the channel.

The invention discloses an unmanned aerial vehicle circling radius optimization method based on a heuristic algorithm. The method comprises the steps that an unmanned aerial vehicle base station provides communication coverage for a target area; the unmanned aerial vehicle base station circles over a target area to provide wireless services for ground users; the number, location and QoS requirement of the ground users are reported: the unmanned aerial vehicle acquires the circling height of the unmanned aerial vehicle through the QoS requirement, and the circling period is divided according tothe number of the users; the unmanned aerial vehicle allocates time slots to the users: the unmanned aerial vehicle uses the heuristic algorithm to jointly optimize the time slot allocation scheme ofthe ground users and the circling radius of the unmanned aerial vehicle; the unmanned aerial vehicle feeds back the time slot allocation result to the ground users; an unmanned aerial vehicle base station forwards the time slot allocation scheme to the ground users, and the unmanned aerial vehicle circles with the optimal radius; the ground users access the unmanned aerial vehicle network; and the users successively access the network in time slots according to the time slot allocation scheme of the unmanned aerial vehicle until all users complete communication. The method provided by the invention has the advantages of quick optimization of the circling radius of the unmanned aerial vehicle and good user fairness.

The invention discloses a method for intelligently forecasting the wind speed in a wind power station. The method comprises the following steps of acquiring and inputting data, layering data sequences, establishing models, forecasting and comprehensively calculating, and outputting forecasting results, wherein in the step of layering data sequences, the original unstable wind speed is decomposed into two stable wind speed data outputs by adopting a wavelet packet decomposition method, and the number of the wind speed data outputs is defined as the number of wind speed sequence layers; in the step of establishing mathematical models, each layer of data in the wind speed sequence layers are independently processed, a BP (back propagation) neural network model is established for the high-frequency sequence layer, high-frequency data are calculated and then enter a data stack; a time sequence model is established for a low-frequency layer, the low-frequency data are calculated and then enter the data stack; after entering the data stack, all the data in the data stack just enter the forecasting and comprehensive calculating step for weighting calculation, and finally the forecasted results are output. The method provided by the invention belongs to an intelligent method, and can be used for realizing multi-step advance forecasting.

The invention discloses an automatic balance control method based on APSO-BP for double counterweight plates. Based on a BP neural network, balance data of each time are fully utilized and the balancing parameters are fitted and optimized to realize automatic balancing control. The input parameters are the initial position A1 and B1 of the two balancing blocks, rotating speed N, unbalance force (shown in the description), the optimization objective is system vibration response (shown in the description), and the optimal output is the target positions A2, B2 of the two balancing blocks. Basedon the initial position, rotational speed and system vibration response of the counterweight, the BP neural network is used to fit the system vibration value, and the adaptive particle swarm optimization is used to obtain the optimal target position of the two counterweight blocks. The invention can effectively optimize the particle swarm optimization based on the neural system fitting input-output relationship by using the improved adaptive weight adjustment method, which is conducive to promoting global optimization, accelerating the convergence rate and increasing the accuracy of the automatic balance system control and the effectiveness of the data.

The invention provides a guidance tool error identification method based on particle swarm algorithm, which comprises the following steps: 1 acquiring data information, wherein the data information comprises flight trajectory data, prior information data and impact point deviation information data; 2, preprocessing that data information obtained in the step 1; 3, setting parameters; 4, identifyingerror coefficients of that guidance tool through the particle swarm algorithm; 5, if that termination condition is satisfied, re-checking the result and outputting the identification result of the error coefficient of the guidance tool; otherwise, returning to the step 4. By applying the method of the invention, the error coefficient of the guidance tool is obtained by forward searching calculation based on the trajectory recurrence instead of linear inverse solution, the technical bottleneck of seriously ill-conditioned environment function matrix in the parameter estimation method of the linear model can be avoided, and the method has the advantages of efficiency and robustness.

The invention provides a frame rate adjusting method and device, and the method comprises the steps: obtaining the recognition time consumption of a first algorithm for an extracted current video frame; obtaining the CPU occupancy rate of a central processing unit; according to the identification time consumption and the CPU occupancy rate, adjusting the frame extraction rate, the problems that the frame rate processed by a fixed algorithm is not suitable for different scenes and the flexibility is poor in the related technology can be solved, the frame sending number and the algorithm effectare improved in a scene with less targets (low pressure), and the frame sending number is reduced in a scene with more targets (high pressure). A smooth picture is ensured. The method can be flexiblyapplied to different scenes.

The invention discloses a universal descent search method for signal detection of a large-scale MIMO system. The method comprises the following steps: S1, constructing a linear inequality quadratic optimization model for detecting transmitting signals of the large-scale MIMO system according to parameter situations under a non-ideal channel, and constructing a detection matrix A; S2, adding a mapping operation to a descent search algorithm according to the model constructed in the step S1; S3, calculating a lower triangular matrix L according to a SSOR preprocessing algorithm; S4, according tothe matrix L, detecting a receiving signal matrix output by a receiving end matched filter through the descent search algorithm containing the mapping operation, and obtaining a transmitting signal estimated value. Compared with the existing iterative detection method, the method provided by the invention is suitable for a more variable channel model and a constantly expanding system scale, stillcan keep low error rate under low signal-to-noise ratio in bad conditions, and can satisfy requirements on detection technologies of the next generation mobile communication better.

The invention discloses a webpage sampling method oriented at barrier-free webpage content detection. The webpage sampling method includes capturing URLs (uniform resource locators) of all related webpages of a website according to a to-be-detected URL, creating an MDL (minimum description length) model for URL sets of the website, clustering all the URLs by an MDL-based greedy algorithm, performing random sampling on each cluster according to a sampling proportion lambada to acquire samples, and performing conventional barrier-free compliance detection on sample sets.

The invention relates to a line feature simplification method based on the clonal selection algorithm. The clonal selection algorithm is used for solving the line feature simplification problem by fully using the advantage of optimization-problem solving of the clonal selection algorithm; according to the characteristic of line feature simplification problem, the affinity function and the constraint condition of the clonal selection algorithm are designed, and a clonal selection algorithm model applied to the line feature simplification is constructed; the local search algorithm and the clonal selection algorithm are combined and the global search capacity of the clonal selection algorithm and the local optimization capacity of the local search algorithm are fully utilized so that the line feature simplification precision and algorithm are improved. The invention provides a new thinking direction for promoting the line feature simplification technique system to develop automatically and intelligently and enriches related research content and methods. The research results provide important theoretical guidance and technical support for the cartographic generalization of actual production.

The invention relates to a frequency utilization planning method and device based on multi-objective optimization, computer equipment and a storage medium. The method comprises the following steps: introducing a multi-objective optimization theory, establishing a multi-objective frequency planning model by taking minimum interference conflicts, highest demand satisfaction and lowest adjacent channel risk as optimization objectives, and proposing a non-dominated sorting ant colony algorithm for solving a frequency planning problem. In the ant colony initialization stage, a hill climbing algorithm with a greedy strategy is used for obtaining a suboptimal solution set so as to improve the early-stage convergence speed of the ant colony; and the frequency equipment is clustered by using a community detection mechanism, so that the calculation complexity of electromagnetic interference analysis is reduced, and the algorithm process is accelerated. Meanwhile, scheduling improvement operation is performed on the obtained frequency utilization planning scheme in each iteration of the algorithm, and parameters such as pheromone volatilization coefficients are adaptively adjusted, so that the global optimization performance of the algorithm is improved. According to the invention, multiplexing of the time domain of the frequency equipment is considered, overall scheduling of dimensions such as the spatial domain, the frequency domain and the energy domain is achieved, and the effect is better.

The invention provides a hybrid expert reinforcement learning method and system, which utilize a multi-head agent with shared network parameters as a plurality of experts and train the agent through adeep deterministic strategy gradient algorithm so as to learn a series of similar tasks at the same time. Each expert is introduced into an uncertainty estimate of performing an action in a state toenhance the anti-overfit Q-value assessment capability and the overall performance of the model. These enable agents to extract, migrate, and share learned knowledge (feature expressions) among different tasks, thereby improving learning efficiency of a single task and effectiveness of scheduling experts among a plurality of tasks. Different from the traditional data-driven design of a hybrid expert system, the hybrid expert system adopts a self-supervised gating network to determine an expert with the highest potential to process each interaction of an unknown task, and can completely calibrate the scheduling accuracy through uncertainty estimation fed back by the expert system under the condition of no man-made external supervision information.

The invention relates to a locality sensitive hash image retrieval parameter optimization method based on empirical fitting, which comprises the following steps of: S1, defining a locality sensitive hash function family H; S2, assuming that k is the number of locality sensitive hash functions and L is the number of hash index tables, when values of L, r and w are determined, calculating a value ofk; S3, taking k functions from the H, and defining a k-dimensional locality sensitive ash function family G; and S4, taking L hash functions from the G, and establishing L hash index tables. According to the invention, a locality sensitive hash image retrieval parameter optimization empirical formula is obtained by a regression analysis method, and by using the empirical formula, calculation steps can be effectively reduced, complexity of parameter optimization of an algorithm can be reduced, and operation efficiency of the algorithm can be improved. Meanwhile, the locality sensitive hash image retrieval parameter optimization method disclosed by the invention is approximate to the theoretical optimum and can enable the algorithm to obtain a high F1 so as to obtain excellent algorithm performance.

The invention discloses a force-oriented graph layout method based on community discovery and clustering optimization, and relates to the technical field of visual layout of graph data. Comprising the following steps: converting original data into corresponding graph data; dividing nodes of the graph data into leaf nodes and non-leaf nodes, regarding each non-leaf node as a community, and compressing the leaf nodes to obtain compressed graph data; performing a community discovery process of maximizing modularity in a first stage of a traditional Louvain algorithm on the compressed graph data; replacing the iterative community merging process in the second stage of the traditional Louvain algorithm with selective community merging for the updated graph data in the previous step; and for the community structure obtained in the step 4 and the corresponding graph data, realizing the force-oriented graph layout based on clustering optimization by using a CombboForce layout algorithm. According to the method, the layout efficiency of the force-oriented graph layout during visual layout of the graph data is improved, and the layout effect of the force-oriented graph layout during visual layout of the graph data is optimized.

A system for detecting sleep apnea uses a PPG sensor for generating a PPG signal. An estimate of respiratory effort is derived from the PPG signal, and from this a respiratory effort signal is derived. Characteristic features are extracted from the respiratory effort signal, and sleep disordered breathing events are detected from the extracted characteristic features. This analysis can distinguish between sleep disordered breathing events and normal breathing during sleep.

The invention discloses a three-dimensional reconstruction method, a face three-dimensional reconstruction method and related devices thereof. The method comprises the following steps: acquiring an image comprising a target object; obtaining feature parameters of the target object based on the image; performing convolution calculation on the feature parameters by using a neural network to obtain three-dimensional reconstruction parameters of the target object, wherein the neural network is obtained by solidifying the association relationship between the feature parameters and the three-dimensional reconstruction parameters; and performing three-dimensional reconstruction on the target object based on the three-dimensional reconstruction parameters. Through the method, power consumption and memory consumption caused by an algorithm in a three-dimensional reconstruction technology are reduced.

The present invention relates to a fertilization precision control method for water and fertilizer integrated equipment and a control system thereof. The method comprises the following steps: step S1, establishing a fertilization precision control model of the water and fertilizer integrated equipment; and step S2, solving an optimal solution of the fertilization precision control model by adopting estimation of distribution algorithms.

A method for processing a CR algorithm by actively utilizing a shared memory of a multi-processor, and a processor using the same are provided. A processor includes: a first multi-processor configured to process a first group of elements of a matrix in accordance with an algorithm; a second multi-processor configured to process a second group of the elements of the matrix in accordance with the algorithm; and a third multi-processor configured to process a third group which comprises some of the elements of the first group, some of the elements of the second group, and some of the elements which are not comprised in the first group and the second group, in accordance with the algorithm. Accordingly, a TDM having many elements can be calculated fast.