2541results about How to "Reduce running time" patented technology

Systems or apparatus, methods, data structures and/or computer programs are provided for implementing an OLAP system that has increased query execution speed, requires reduced data storage capacity for an OLAP system and/or facilitates scaling of an OLAP system to large data cubes. The OLAP system can advantageously be implemented as an all-main memory OLAP system.

The invention discloses an automatic image defogging method based on dark primary color, which is used for solving the problem of information loss because the traditional defogging method highlights details by improving foggy day image contrast. The method provided by the invention comprises: A. calculating the dark primary color of the primary fog images and relevant atmosphere light value; B. according to the luminance component image of the original fog image, calculating a transmission image reflecting local fog concentration in an atmospheric scattering model; and C. determining the defogged primary image according to the fog image, the transmission image and the atmosphere light value in the atmospheric scattering model. Because of being built on the basis of a physical model, the invention can process various fog images in a self-adaption mode; and defogged images have favorable edge details and ideal contrast, and the clarifying effect is superior to the traditional defogging method based on image enhancement.

Systems and methods are described for updating an influence model used to manage physical conditions of an environmentally controlled space. A method comprises operating an environmental maintenance system in a first production mode with the influence model until an event causes the system to enter a second production mode. In the second production mode a first actuator's operation level is varied and operation levels of other actuators are optimized. The influence model is adjusted based on the operation levels.

The invention provides an intelligent prescription auditing system and an intelligent prescription auditing method. The system is provided with an input module, various databases, a prescription auditing module, a dispensing explaining module, and an output module. The method is implemented as follows: a doctor refines a prescription through the input module and then sends the prescription to the prescription auditing module; after receiving the prescription, the pharmacist reviews writing rules, indications, dosage, incompatibility and drug interactions of the prescription according to various databases; the doctor returns checking comments of the disqualified prescription to the prescription input module so as to modify or re-issue the prescription or give suggestions, and sends the qualified prescription to the dispensing explaining module; after receiving the prescription, an allocation doctor allocates medicines; after receiving the prescription, a dispensing doctor generates prescription information, a medication explaining matter and the like of dispensing explaining, which are printed by the output module and then explained to a patient. The invention provides a medicine dispensing control system and a medicine dispensing control method which are capable of effectively avoiding great prescription, irregular prescription, unsuitable medication for the prescription and unconventional prescription.

The invention discloses a deep convolutional neural network-based abnormal crowd behavior visual detection and analysis early warning system. The system comprises a camera mounted at a monitoring target facility, a security prevention cloud server and an abnormal crowd behavior visual detection and analysis early warning system. In the system, various human body objects in the target facility areextracted through a deep convolutional neural network technology; then motion states of human bodies are calculated, identified and judged by using an optical flow method; different states of the human body objects are subjected to clustering and crowd modeling; further crowd objects are subjected to density calculation and danger index calculation; and finally according to different combinationsof crowd density, motion vector values and duration quantitative index data, various abnormal crowd behaviors are identified and judged, and according to the states of the abnormal crowd behaviors, corresponding crowd gathering management control policies are enabled. The deep convolutional neural network-based abnormal crowd behavior visual detection and analysis early warning system provided bythe invention is unlimited in scale, relatively high in precision and relatively good in robustness, and is based on a deep convolutional neural network.

The invention discloses a resource scheduling method under a Hadoop-based multi-job environment, which includes: (1) collecting the three-party monitoring information of cluster loads, a Hadoop platform and hardware in real time; (2) collecting the job execution monitoring information of a user on each computing node of a cluster in real time; (3) gathering the three-party monitoring data of the cluster, modeling to evaluate the computing capabilities of the nodes, and dividing the nodes of the cluster into superior computing nodes and inferior computing nodes; (4) if the nodes are the superior computing nodes, then starting a job task resource demand allocation policy based on similarity evaluation; (5) if the nodes are the inferior computing nodes, then returning to a default resource demand allocation policy of the Yarn. The resource scheduling method under the Hadoop-based multi-job environment solves the problem of resource fragments caused by oversize job resource demand division granularity in conventional resource schedulers of the Yarn, can comprehensively take the heterogeneity of cluster nodes and jobs into consideration, and increases the execution concurrency of the cluster by reasonably and effectively allocating the node resources, thus increasing the execution efficiency of the multiple jobs of the Hadoop cluster.

The invention provides a six-camera around looking-based cylindrical panoramic generation device and method. The device comprises a regular hexagonal prism enclosure; six cameras are horizontally and outwardly arranged at the centers of the six side surfaces of the regular hexagonal prism; and the six cameras are focus-fixed fisheye cameras with 90-degree horizontal view angles. The method comprises the following steps: capturing images of the six cameras simultaneously; carrying out distortion correction on the six images by using a reverse division model; carrying out cylindrical projection on the six corrected images; carrying out feature detection and matching on the adjacent images with superposed areas by using an SIFT feature point detection algorithm; calculating the image translation splicing parameters and determining the sizes of the superposed areas through the optimum matching point; carrying out image splicing and fusion simultaneously; and carrying out smooth transition on the public area by using a slow-in slow-out fusion method so as to finally obtain the naturally transitioned cylindrical panoramic images. According to the device and method, the natural panoramic images of 360-degree view can be efficiently generated, so that the device and method are suitable for panoramic monitoring.

An apparatus for spectrometry that includes a spectrometer configured for second order focusing and capable of 2π azimuthal collection.

The present invention relates, e.g., to a method for detecting a nucleic acid molecule of interest in a sample comprising cell-free nucleic acids, comprising fluorescently labeling the nucleic acid molecule of interest, by specifically binding a fluorescently labeled nanosensor or probe to the nucleic acid of interest, or by enzymatically incorporating a fluorescent probe or dye into the nucleic acid of interest, illuminating the fluorescently labeled nucleic acid molecule, causing it to emit fluorescent light, and measuring the level of fluorescence by single molecule spectroscopy, wherein the detection of a fluorescent signal is indicative of the presence of the nucleic acid of interest in the sample.

The invention provides an LTE-based radio resource allocation method of Internet of vehicles. The method comprises the following steps: step S1, dividing priority for node communications according to node communication service characteristics and interference of node to a base station; step S2, building a position division model, wherein the base station dispatches D2D users in sequence according to the priority and selects candidate resource share nodes according to distance and link loss factors; and step S3, selecting cellular user resources with smallest interference in a region where the candidate resource share nodes are located as cellular link resources for D2D communication multiplex. the number of nodes needed to be fed back to the base station in the invention is obviously smaller than that in optimal algorithm, thereby largely reducing the load of the base station; in addition, the operating time of the algorithm used in the invention is obviously shorter than that of the optimal algorithm, thereby effectively reducing processing delay.

Provided is a multitask fake plate vehicle vision detection system based on deep convolution nerve network, comprising a camera disposed on a city road, a traffic cloud server, and a fake plate vehicle reverse driving detection system. The camera is used for obtaining the snapshot image data at the city road and is disposed above a road. The traffic cloud server is used for receiving the road video data obtained from the camera, and submitting the road video data to the fake plate vehicle detection system for detection and identification. The fake plate vehicle detection system comprises a Faster R CNN-based vehicle positioning detection module, a vehicle type identification module, a fake plate positioning and identifying module, a license plate legality detection module, a logic consistency detection module, a vehicle inspection mark fine comparison module, and an alarm notification generating module. The invention also provides a multitask fake plate vehicle vision detection methodbased on deep convolution nerve network. The invention is advantageous in that vehicles with fake plates can be rapidly and accurately positioned, and the efficiency of criminal detection can be improved.

The invention discloses an indoor positioning method based on a distributed AP selection strategy. The method comprises the following steps of using mobile equipment to collect k times of signal intensities of AP nodes on each reference point and processing data; equally dividing a target area into m subareas, classifying fingerprint information of the different subareas, using a fingerprint information base to train a subarea module and selecting the corresponding subarea according to RSS fingerprint information; calculating a correlation of each AP node and each subarea, sorting correlation coefficients, selecting the AP node whose correlation coefficient is greater than Ptau as a positioning node of the subarea; in each subarea, taking the selected positioning node as input of a DBN model and a corresponding position point as output of the DBN model, training the DBN model and constructing a positioning prediction model. By using the method, big noise and the AP node with a weak position distinguishing capability can be effectively removed; positioning precision of an overall WIFI indoor positioning system is increased and algorithm operation time is shortened.

The invention discloses a method of using intelligent glasses for vehicle maintenance, which belongs to the field of vehicle maintenance and aims at solving the technical problem that as intelligent glasses can not be used for vehicle maintenance, intelligence is not high. According to the technical points, the method comprises a step of head pose recognition on the intelligent glasses for vehicle maintenance, a step of sharing of other equipment data for vehicle maintenance by the intelligent glasses, a step of vehicle remote maintenance support by the intelligent glasses, a step of using of an intelligent glass touch board during the vehicle maintenance process, and a step of hand gesture recognition by the intelligent glasses during the vehicle maintenance process. The method of the invention has the advantages that applications of the intelligent glasses in the vehicle maintenance field can be realized.

The invention discloses a camera motion and image brightness-based Kinect depth reconstruction algorithm. The algorithm comprises the steps of 1) uploading data collected by Kinect to a computer through a third-party interface under the condition that a Kinect depth camera and an RGB camera are calibrated and aligned; 2) recovering a three-dimensional scene structure and a motion track of the kinect RGB camera from an RGB video sequence, and obtaining a relationship between point cloud and a camera motion; and 3) reconstructing image depth by utilizing brightness status information of an image in combination with the relationship between the point cloud and the camera motion, obtained in the step 2). According to the algorithm, the depth camera does not need to be improved physically, a complex apparatus combination does not need to be designed, and an illumination calibration step which is often used in a conventional depth reconstruction method, generally only can be limited in laboratory conditions, does not have a practical application value and is complex and strict in condition is not needed, so that compared with the conventional method, the algorithm has higher practical application value and significance.

The invention discloses a visual capture method and device based on a depth image and a readable storage medium. The method comprises steps that a point cloud image is acquired through a depth cameraKinect, the acquired point cloud image is segmented through an RANSAN random sampling consensus algorithm and an Euclidean clustering algorithm, and an identification-needing target object is acquiredthrough segmentation; 3D global characteristics and color characteristics of the object are respectively extracted and are fused to form a new global characteristic; off-line training of a multi-class support vector machine classifier SVM is carried out through utilizing the new global characteristic of the object, category of the target object is identified through utilizing the trained multi-class support vector machine classifier SVM according to the new global characteristic; then the category and the grasping position of the target object are determined; and lastly, according to the category of the target object and the grasping position of the target object, a manipulator and a gripper are controlled for grasping the target object to the specified position. The method is advantagedin that the target object can be accurately identified and grasped.

A method for optimizing efficiency and power consumption in a hybrid computer system is disclosed. The hybrid computer system may comprise one or more front-end nodes connected to a multi-node computer system. Portions of an application may be offloaded from the front-end nodes to the multi-node computer system. By building historical profiles of the applications running on the multi-node computer system, the system can analyze the trade offs between power consumption and performance. For example, if running the application on the multi-node computer system cuts the run time by 5% but increases power consumption by 20% it may be more advantageous to simply run the entire application on the front-end.

In an encoder for encoding symbols of data using a computing device having memory constraints, a method of performing a transformation comprising loading a source block into memory of the computing device, performing an intermediate transformation of less than all of the source block, then replacing a part of the source block with intermediate results in the memory and then completing the transformation such that output symbols stored in the memory form a set of encoded symbols. A decoder can perform decoding steps in an order that allows for use of substantially the same memory for storing the received data and the decoded source block, performing as in-place transformations. Using an in-place transformation, a large portion of memory set aside for received data can be overwritten as that received data is transformed into decoded source data without requiring a similar sized large portion of memory for the decoded source data.

Bearing fault diagnosis based on pseudo-tag semi-supervised kernel local Fisher discriminant analysis is provided. The method is characterized in comprising the following steps: (1) collecting vibration signals of bearings under different working conditions to form a training sample; (2) performing feature extraction on the training sample obtained in (1); (3) performing normalization processing on features obtained in (2); (4) obtaining a clustering tag set by using density peak clustering for the entire feature set obtained in (3); (5) using clustering pseudo-tags obtained in (4) to construct local inter-cluster divergence and intra-cluster divergence regularization terms, and combining the regularization terms with the inter-class divergence and intra-class divergence with tag samples in the FDA to determine a final projection vector; (6) using the final projection vector obtained in (5) to solve a projection set of the tagged feature set in the dimensionality reduction space; (7) using the projection set obtained in (6) to train an extreme learning machine; and (8) performing processing of (2), (3) and (5) on the collected vibration signals, and inputting the processed vibration signals to determine the working conditions. The technical scheme of the present invention is applied to the problem of fault identification of bearing equipment.