166 results about "Search problem" patented technology

A hybrid of grid-based and graph-based search computations, together with provision of a sparse search technique effectively limited to high-probability candidate nodes provides accommodation of path constraints in an optimization search problem in substantially real-time with limited computational resources and memory. A grid of best cost (BC) values are computed from a grid of map cost (MC) values and used to evaluate nodes included in the search. Minimum segment/vector length, maximum turn angle, and maximum path length along a search path are used to limit the number of search vectors generated in the sparse search. A min-heap is preferably used as a comparison engine to compare cost values of a plurality of nodes to accumulate candidate nodes for expansion and determine which node at the terminus of a partial search path provides the greatest likelihood of being included in a near-optimal complete solution, allowing the search to effectively jump between branches to carry out further expansion of a node without retracing portions of the search path. Capacity of the comparison engine can be limited in the interest of expediting of processing and values may be excluded or discarded therefrom. Other constraints such as approach trajectory are accommodated by altering MC and BC values in a pattern or in accordance with a function of a parameter such as altitude or by testing of the search path previously traversed.

A solution is provided that allows end users to search documents in multiple remote content repositories in an enterprise environment, while maintaining security of those documents. Additionally, end users are able to search for a document without knowing which server or directory it is stored under. This is accomplished by installing a search agent on the end user's machine. The search engine collects the metadata of authorized documents located in different repositories via a web protocol (such as Hypertext Transfer Protocol, or HTTP) and stores them in the user's local memory as “searchable links”. This solves the enterprise content search problem through the creation of searchable links of remote content objects in an end user's local machine while allowing emerging desktop-search technology to index them.

The invention discloses a sparse dimension reduction-based spectral hash indexing method, which comprises the following steps: 1) extracting image low-level features of an original image by using an SIFT method; 2) clustering the image low-level features by using a K-means method, and using each cluster center as a sight word; 3) reducing the dimensions of the vectors the sight words by using a sparse component analysis method directly and making the vectors sparse; 4) resolving an Euclidean-to-Hamming space mapping function by using the characteristic equation and characteristic roots of a weighted Laplace-Beltrami operator so as to obtain a low-dimension Hamming space vector; and 5) for an image to be searched, the Hamming distance between the image to be searched and the original image in the low-dimensional Hamming space and using the Hamming distance as the image similarity computation result. In the invention, the sparse dimension reduction mode instead of a spectral has principle component analysis dimension reduction mode is adopted, so the interpretability of the result is improved; and the searching problem of the Euclidean space is mapped into the Hamming space, and the search efficiency is improved.

A system and method for decentralized cooperative control of a team of agents for geographic and other search tasks. The approach is behavior-based and uses probability particle approach to the search problem. Agents are attracted to probability distributions in the form of virtual particles of probability that represent hypotheses about the existence of objects of interest in a geographic area or a data-space. Reliance on dependable, high-bandwidth communication is reduced by modeling the movements of other team members and the objects of interest between periodic update messages.

The invention discloses a monitoring system uniform platform of on-line monitoring and auxiliary equipment of a transformer substation. The monitoring system uniform platform is characterized by comprising three platforms including a transformer substation intelligent terminal, an municipal level auxiliary monitoring platform and a provincial level monitoring platform, wherein the three platforms adopt a mesher deploy, and service interfaces are used for the interactions among the platforms; the platforms adopt a WebService mode to interact and access through uniform access entries; and meanwhile, the three platforms are mutually independent and respectively provided with an individual access entry. The invention can finish the uniform monitoring and acquisition of monitoring data at a station end and provide the uniform monitoring and application service of monitoring equipment at the station end so as to be convenient for an operator to find out problems timely, for an maintainer to analyze and search problems with the assistance of more accurate data during failure and for a manager to carry out statistic analysis on various information of the equipment of the station end to judge the performance of the equipment, evaluate the condition of the equipment, plan and prepare a plan in advance and reduce the probability of accident occurrences.

The invention discloses a potential game-based multi-unmanned aerial vehicle cooperative search method. The method comprises steps: 1, modeling of a multi-unmanned aerial vehicle cooperative search problem is carried out; 2, potential game modeling for a multi-unmanned aerial vehicle cooperative motion and potential game solution in a double log-linear learning method are carried out; 3, a probability map is updated according to sensor detection information, information fusion is carried out on the updated probability map, and a target existence probability is acquired; and 4, uncertainty is updated according to the target existence probability, and multi-unmanned aerial vehicle cooperative search is carried out. The method of the invention can realize multi-unmanned aerial vehicle cooperative search, and comprises steps of cooperative motion based on the potential game, updating of the probability map, information fusion and the like, and due to the self distributed control mode, the calculation is simple, the robustness is strong, and external disturbance can be effectively handled.

The invention relates to a novel direct-current converter valve test unit and a test method thereof. The test unit comprises an industrial computer, a surge voltage unit, a low-voltage triggering test unit, an impedance tester LCR, a 24V power supply, a valve voltage-current waveform acquisition system, a keyboard mouse, a display unit and an over-voltage and over-current protection unit. After a test is performed, the display unit can display each testing voltage-current waveform, and if a test is failed, the novel direct-current converter valve test unit is convenient to analyze and search problems.

A system and related techniques generate a survey to capture user feedback about the quality of search results, in a continuous context with the user's Web page or other search activity. According to embodiments, a survey frame inviting the user to undertake a set of search questions may be presented within the same set of page frames which display the search results, so that the user may choose to answer the survey while still viewing their search results, or selected Web sites or other hits. According to further embodiments, rather than being presented within the frame structure of a page, the survey may be presented from within a browser toolbar extension, side-by-side or otherwise arranged within the environment of the user's search activity. Unlike other feedback gathering platforms which may force the user to navigate to a new page to view and respond to questions, or transmit email questionnaires after the fact, according to the invention in one regard the user may be prompted into a dialogue to supply feedback about their search experience, while still within the contextual workflow of that experience, and still being able to view or review results or content which they have received. User distraction is therefore minimized while feedback quality may be improved. The user feedback which rates the quality or accuracy of the search results or search experience may in embodiments be stored and used to train search intelligence, or for other purposes.

The invention relates to a descriptive information matching similarity-based resource service matching and searching method. Specifically, in the method, after digital manufacture resource service description, resource service descriptive information is further divided into types of character body concept, sentence, digital value, entity concept and the like, and corresponding similarity matching algorithms are designed respectively; and on such a basis, resource service matching searching is realized by the steps of basic matching, input and output matching, quality of service matching and comprehensive matching. The method mainly aims to solve the problem that the matching and searching of the descriptive information types of the digital value, sentence, entity concept and the like are not taken into account in the conventional resource service matching. In the method, a manufacture resource service matching and searching realization frame, the similarity matching algorithm for the different types of service descriptive information, a multilayer resource service matching model and realization algorithms are involved. The method has the advantage that: a distributed system supporting semantics and SOA is hierarchically clear and has high compatibility and expansibility.

A method and system is provided for generator successor nodes in an external-memory search of a graph having a plurality of nodes and outgoing edges of the plurality of nodes. The method and system includes construction of an abstract representation of the graph to include a set of abstract nodes and abstract outgoing edges of the abstract set of nodes. The edges of the graph are partitioned based on the abstract representation of the graph. The partitioning includes grouping edges (or instantiated operators of a search problem) that map to the same abstract edge of the abstract representation together as isomorphic edges. Successor nodes along outgoing edges that belong to the same group are generated consecutively. By this design, partial and incremental expansion of nodes can be achieved in order to limit the number of time-consuming I/O (or other communication) operations in search algorithms that take advantage of the memory hierarchy in a computer system, such as those that use disk storage.

The invention discloses a multi-mode multi-standard path search method based on genetic algorithm, which is realized by computer. The method solves the multi-mode multi-standard traffic path search problem by defining genetic operator, matching corresponding strategy and utilizing the optimal theory. Under various trip standards, the method is used to solve, which does not have to set the weight value of each standard in advance, avoids no-solution risk brought by constraint method and reduces the influence of personal subjective factors. The path output result of the invention can satisfy various trip requirements and has the characteristic of various trip mode combinations, provides personalized and various trip projects for traveler and provides good technical support for the public trip information service.

The invention discloses a multi-unmanned aerial vehicle collaborative searching method based on improved pigeon flock optimization. The method comprises the following steps: firstly establishing a mapmodel, and establishing a target information map by using a Markov model, and then establishing a motion model and a digital pheromone map of the unmanned aerial vehicle; performing the collaborativesearching of multi-unmanned aerial vehicle by applying the pigeon flock optimization algorithm, and realizing by using chaotic and reverse strategies at an initial population location, thereby determining the randomness of the initial location; when performing the iteration of the map and the compass operator at the first stage, preventing from falling in local optimum by using Cauchy mutation; when performing the iteration of a landmark operator, representing the bad individual and Gaussian variation by using simulated annealing reservation part, thereby preventing premature from falling inlocal optimum. The chaotic and reverse strategies are used for initializing the population location, the variation and simulated annealing algorithm imported for preventing local optimum are combined,thereby solving the dynamic target and repeated searching problem in the searching process, and the searching efficiency is effectively improved at the same time.

The invention discloses a word-segmentation organizing method and a clustering method for a ciphertext search. The word-segmentation organizing method and clustering method solves an indexing and searching problems of a private document of a user on a public server; word segmentation, feature extraction and summarization are carried out to the private document by a client end, and encryption is carried to segmented words, the document and a summary, so that the privacy of the private document of the user on the server is ensured; and the encrypted document is stored and clustered on a server end, an index is built for convenience in searching, so that searching requests of the user are responded and secondary clustering of the ciphertext is achieved. The word-segmentation organizing method and the clustering method has the characteristics of segmenting the words in the document based on the client end, storing and clustering based on the ciphertext and searching and clustering based on the ciphertext.

The invention discloses a large-scale Hash image retrieval method based on deep representation learning. The method comprises the steps of data preprocessing, construction of a convolutional neural network layer based on semi-supervision, design of a loss function based on paired Hash loss functions, optimization learning of the loss function and post-processing of a search result. According to the invention, the deep convolutional neural network layer and the full connection layer are adopted to carry out image feature extraction and hash function learning. A combined loss function composed of a cross entropy loss item with a label. A triple loss item and a pseudo label loss item is designed. A stochastic gradient descent method of a drive quantity is adopted for optimizing and solving. High calculation efficiency is achieved, and finally image retrieval performance with uniform precision and speed is achieved. Under the current situation of rapid development of the image search field, efficient modeling is carried out on the image search problem on the basis of an image data structure and a label, and the model accuracy and the more optimized query speed are effectively improved.

Disclosed is an excavating diagnosis method of failure data based on a bayesian network. The method is used for diagnosis of equipment failures. Parameter points, capable of being obtained, of existing devices with failures are monitored in advance, and a monitoring signal and a failure type are used as input variables of the bayesian network; then, nodes are sorted at random, side adding or changing operation is carried out in the search problem, the generated bayesian network is graded and stored in a database, finally, the bayesian network with the highest score in the database is used as an equipment failure diagnosis inference graph, equipment data needed to be diagnosed are input into the diagnosis inference graph, and the diagnosis result can be obtained. The search density can be set according to the actual condition, and can be prevented from being caught in endless loop; a penalty term can be adjusted so as to prevent excessive fitting; an initial sequence is generated at random to enable the system to have a certain roughness; a user can see the equipment failure diagnosis inference graph, and therefore logic analysis can be carried out on the failure happening mechanism.

The invention discloses a big data intelligent search method and system based on a whale optimization algorithm. The whale optimization algorithm is utilized for optimizing and solving big data intelligent search problems, so that search results closest to user needs are obtained rapidly, and the accuracy and efficiency of intelligent search engines are further improved. Accordingly, a high-quality feasible solution of the big data intelligent search problems can be found within the acceptable time cost; according to search conditions of a user, big data related to keywords input by the user is obtained from an engine database, the user marks the search results continuously, and the search effect meeting the personalized requirements of the user is achieved gradually. The whale optimization algorithm is utilized for conducting big data intelligent search, one intelligent search engine is built on the basis of the body characteristics, the search results most conform to the requirementsof the user are found rapidly and efficiently, the problem that the current search engine cannot well provide interested information for the user is solved, and the search efficiency is high.

The invention discloses a half-connection inquiry optimizing method based on periphery searching and a system of the half-connection inquiry optimizing method. Inquiry information of a user is converted to a traversing back tree-shaped half-connection searching state picture by an inquiry optimizing engine module. A periphery node collection is produced by the half-connection searching state picture through the reversing searching. An enlightening type evaluation function f(x)=Minx<P(h(x)+h*(x)) is constructed. The optimal half-connection operation sequence explanation is obtained through the front direction searching based on IDA*. The result is delivered to the user. The optimal half-connection operation sequence explanation is obtained when the front direction searching coincides with a periphery node collection P. By means of the half-connection inquiry optimizing searching range is reduced. Complexity of the searching problem is reduced. Requirements of data inquiry optimizing in the data sharing environment can be well met.

The invention relates to an automatic commodity image classifying method and provides an automatic commodity image classifying method based on vision information. The automatic commodity image classifying method comprises the following steps of: extracting local features of an image, and realizing class recognition by adopting a hierarchical clustering and image-class distance computing method. The automatic commodity image classifying method has the advantages that the automatic commodity image classification can be realized according to vision contents of the commodity image; by adopting a method of describing a subcluster by using local features of each class of images, an exhaustion searching problem in the image-class distance computation is greatly reduced and the computing efficiency is greatly improved; and when the distance between image blocks is computed, position information of the features is taken into full account, so that the classifying effect can be improved.

The invention relates to an engine agency search method comprising the following steps of: A. inputting a describing word string of a search problem; B. analyzing the describing word string of the search problem; C. optimizing an agency resource; D. issuing and driving search; and E. processing a search result. The invention provides an important technical approach for establishing a unified search platform which is convenient to use and is based on a vertical search technique by establishing new agency search mechanisms among a plurality of professional vertical engine search groups for user search. With the engine agency search method, the problem of difficult adoption of a plurality of vertical search fine products is effectively avoided, and the high search precision and real-time performance contributed by the vertical search technique are kept.

The present invention provides a network association analysis method and device. The network association analysis method comprises: obtaining calling detailed tracking data and multi-dimensional network data in a network; searching a network abnormal event in the calling detailed tracking data, and determining the grid information of the position where the network abnormal event happens; and performing network data analysis with at least one dimension of the position grid of the network abnormal event according to obtained multi-dimensional network data and the position grid information, and obtaining the network problem existed at the position where the network abnormal event happens. The problems are solved that the network problem is analyzed only in one dimension and the problems in the network cannot be accurately and rapidly solved in the prior art; and moreover, the network association analysis method and device are able to assist network optimization engineers in rapidly searching problems and provide a good method for the maintenance and normal operation of the network.

The invention discloses a social network association searching method based on a graphics processing unit (GPU) multiple sequence alignment algorithm. The method comprises the following steps that: a central processing unit (CPU) performs web crawler on an individual webpage so as to extract an individual characteristic vector from a social network; the CPU filters redundant characteristic information from the individual characteristic vector so as to generate a uniform individual characteristic information vector base; a GPU calculates an individual distance matrix and a correction distance matrix of the social network according to the uniform individual characteristic information vector base; the GPU establishes a social network association route guidance tree according to the correction distance matrix; and the GPU traverses the social network association route guidance tree so as to perform the optimal association route searching. By utilizing the advantage that the GPU is suitable for processing a large amount of dense data, associated searching problems which are solved by the the multiple sequence alignment algorithm are parallelized, complex and time-consuming operations, such as formation and traversing of the matrixes and the association route guidance tree, are finished by the GPU, and the problem of long time caused by a large amount of social network data and operation complexity is solved.

aiming at the search problem of lots of code phases when the long-period pseudo codes are directly captured, the present invention provides a long-code segmentation overlay local correlation capture method based on the GPU. input data is divided in the segment mode to reduce the scale of data processing and storage requirement, is suitable for GPU realization of different performances, can help a multi-core processor to perform parallel task processing of data while data division, and can fully develop multi-core parallel processing advantage. The long-code segmentation overlay local correlation capture method based on the GPU employs FFT parallel search pseudo code phase and employs hundreds of floating-point calculation units in the GPU to perform batch processing segment data acceleration FFT operation so as to improve the long-code pseudo code phase capture speed and have a great meaning of the satellite navigation spread spectrum signal software.