47 results about "Problem space" patented technology

A method of fusing sensor detection probabilities. The fusing of detection probabilities may allow a first force to detect an imminent threat from a second force, with enough time to counter the threat. The detection probabilities may include accuracy probability of one or more sensors and an available time probability of the one or more sensors. The detection probabilities allow a determination of accuracy of intelligence gathered by each of the sensors. Also, the detection probabilities allow a determination of a probable benefit of an additional platform, sensor, or processing method. The detection probabilities allow a system or mission analyst to quickly decompose a problem space and build a detailed analysis of a scenario under different conditions including technology and environmental factors.

A computer implemented method, apparatus, and computer program product for effectively reducing a complicated problem space to enable faster implementation of system management software, and in particular, policy management for security software. The policy management tool of the present invention receives input from a user to configure a policy model, wherein the policy model is configured according to a set of policy requirements. The policy management tool presents a graphical view of a policy model according to the input from the user, wherein the graphical view allows the user to visualize internals of the policy model as a whole. The policy management tool performs validations on the policy model against requirements of the set of policy requirements. A simulation of the policy model may then be performed to determine the validity of the policy model and generate real test results feedback at a time the policy model is configured.

A new paradigm for security analysis is provided by transitioning code analysis reporting from the problem space (the warnings themselves), to a solution space (potential solutions to the identified problems). Thus, instead of reporting raw findings to the user, the automated system as described here outputs proposed solutions to eliminate the defects identified in the security analysis. A consequence of this approach is that the report generated by the analysis tool is much more consumable, and thus much more actionable. Preferably, the report provides the user with one or more candidate location(s) at which to apply a fix to an identified security problem. These locations preferably are identified by processing overlapping nodes to identify one or more solution groupings that represent an API for a sanitization fix. The report also includes one or more recommendations for the fix, and preferably the report is generated on a per-vulnerability type basis.

The invention relates to the enterprise engineering and the information technology field, and discloses an optimization method for the multi-objective enterprise process model parameters, which is based on the genetic algorithm. In the optimization field of the enterprise process model parameters, most optimization methods use the countermeasure weighing principle to combine each sub-goal into a single objective, so as to process an optimization objective, thus, some defects exist in the comprehensive evaluation process. Aiming mainly at various optimal parameters, such as the production rate of the product, the quantities of various resources, the persistent time of each activity and the configuration schemes for the required resources, as well as the scheduling strategies for the selectable resources, the invention comprehensively evaluates a plurality of indexes, such as the running time, the running cost, the final product quality, the utilization rate of the product, the queue length, etc. The invention adopts the method which divides the problem space in a multi-layer way to process the combinatorial constraint relationship among the various optimal parameters, thereby increasing the flexibility of the optimization parameter selection, and avoiding the analysis calculation to the ineffective parameter combination scheme, furthermore facilitating the maintenance of the group diversity.

A system, and method for dynamically creating web applications from data is described. Simple data is transformed into a declarative application data model that is then parsed by a server and compiled into a functional web application. Application functionality is partly determined by rules applied to web application objects in response to user actions. These rules are triggered by various cues, including user actions and relationships among objects in the web applications. A web application is considered an n-dimensional problem space, and relationships among application objects can be modeled using set theory. The status of a particular relationship among objects and user actions can trigger specific application behavior. Additionally application behavior can be triggered in other ways, like conditions in arbitrary scripts or combinations of multiple triggers combined using logical connectives.

The invention discloses an analysis method for the spreading effect of a requirement change, and belongs to the technical field of software development. The analysis method comprises the steps that requirement dependencies are extracted in the software problem analysis process and modeled according to a requirement dependency diagram; the tracing relation among a real world field in a problem space, user requirements and software products in a machine specification-solution space is described through a tracing matrix; software requirement products affected by the requirement change are recognized through horizontal requirement change spreading effect analysis based on the requirement dependency diagram, and software design products and code products affected by the requirement change are recognized through vertical requirement change spreading effect analysis based on the tracing matrix; the software requirement products, the design products and the code products affected by the requirement change are collected to form a soft product set affected by the requirement change. The analysis method has the advantage that the problem that because the spreading effect of the requirement change is not effectively defined, the requirement change workload is difficult to estimate and requirement change management decisions are improper.

The invention discloses a method for predicting and simulating a network public opinion in an emergency based on an SOAR model. The method comprises the steps of based on the SOAR model, taking net citizens as agents, and taking a net citizen group behavior transformation process in the network public opinion as a continuous transformation process of state with time in a public opinion problem space; in combination with network public opinion development and government response related theories, dividing a net citizen group into Me-formers and In-formers, and dividing a network public opinion development phase into generation, bursting, ripening and attenuation; designing working memory, long-term memory, decision-making process and learning mechanisms of the net citizen agent; constructing a net citizen group behavior transformation rule base; and establishing a net citizen group behavior transformation SOAR agent model based on the SOAR model. The attenuation phase in the network public opinion development process is taken as an example, the prediction and simulation for the network public opinion in the emergency are realized, and thus the influence of different government responses on different types of net citizen group behaviors is evaluated by the method.

The invention provides an intelligent method for optimizing working procedure of a semiconductor packaging production line, applying particle swarm optimization (PSO) to optimize the working procedure in a semiconductor packaging process, modeling and finding the solution, taking processing center as a unit, building a mapping relation between problem to be solved and particle swarm, using particles to express optional solutions to the problem, and finding the optimized solution under the guidance of corresponding target; each particle swarm optimization subproblem is using processing time of the machine on the production line for different products to encode the particles to determine particle swarm optimization space and making time sequence decoupling on the optimization problem to be solved, obtaining the corresponding subproblem and then dividing the problem space and determining a mapping relation between the divided space and each subproblem to find particle swarm optimization solution of each subproblem, thus obtaining an optimized machine processing time allocating solution and machine occupation ratio. The practice indicates that PSO has the advantages simple concept, convenient implementation, rapid convergence, etc.

The invention relates to a directed graph association rule-based music cloud recommendation algorithm. According to the directed graph association rule-based music cloud recommendation algorithm, data modeling analysis is carried out on music cloud operation data so as to provide technological means and services for the music market analysis and aid decision making of commercial operation departments; by mining the maximum frequent item sets, the mining efficiency can be effectively improved and the scale of a problem solution can be effectively reduced; and the algorithm adopts a depth-first search method, nodes in node sets of directed item set graphs are selected as initial nodes to carry out search, and judgement is carried out on the frequent extension sets of each item set, so that the problem space can be effectively reduced, the number of candidate sets of the to-be-checked maximum frequent item sets can be decreased, and the efficiency of the algorithm can be effectively improved. The efficiency of the algorithm can be further optimized and improved by adjusting the arrangement sequence of the node sets of the directed item set graphs, adjusting the generation sequence of directed edges and designing an effective storage manner for the maximum frequent item sets.

The invention discloses a water quality prediction method combining a divide-and-conquer method and water quality periodicity, and belongs to the technical field of water quality prediction. The water quality prediction method aims to solve the problem that complex change rules of water quality data cannot be sufficiently learnt by a single predictor and accordingly prediction accuracy is poor, a problem decomposition solving frame based on data driving is adopted in the method, the frame can automatically divide problem space according to data characteristics in the learning process, the number of predictors does not need appointing by staff, and better robustness is achieved. Predictor effect evaluation and water quality data periodicity are combined, so that change characteristics of water quality data on the time scale are well utilized, and a more accurate problem decomposition algorithm is obtained. According to frame leaning content, data to be predicted are mapped to a predictor group correspondingly having prediction advantages, a prediction result is obtained through weighting of corresponding predictors, and better accuracy and stability are achieved.

The invention discloses a vector model-based massive spatiotemporal data retrieval method and system. The method comprises the steps of performing vectorization processing on data of event space and problem space to obtain a spatiotemporal data vector; performing dimension reduction processing on the spatiotemporal data vector according to a target condition vector needed to be retrieved; performing vector operation on each dimension of the spatiotemporal data vector subjected to the dimension reduction processing and the target condition vector; and judging vector operation results, screening out the vector operation results meeting a preset condition, and obtaining corresponding retrieval results. The system comprises a spatiotemporal data vector representation module, a spatiotemporal data vector dimension reduction module, a spatiotemporal data vector operation module and a retrieval result judgment module. According to the method and the system, the to-be-queried data volume can be reduced, the calculation complexity is greatly lowered, and the retrieval efficiency is effectively improved. The method and the system can be widely applied to the field of retrieval.

Embodiments of the invention provide a method, system and computer program product for log file reduction according to problem space topology. A method for log file reduction according to problem space topology can include receiving a fault report for a fault in a solution executing in memory of one or more computers of a computer data processing system. The method further can include extracting references to at least two resources of the computer data processing system from the fault report. The method yet further can include filtering a set of all log files for the computer data processing system to only a subset of log files related to the at least two resources. Finally, the method can include displaying the subset of log files in a log file analyzer.

Systems and methods for selecting a neural network for a machine learning problem are disclosed. A method includes accessing an input matrix. The method includes accessing a machine learning problem space associated with a machine learning problem and multiple untrained candidate neural networks for solving the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the machine learning problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the machine learning problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the machine learning problem.

The present invention provides a non-reductive normalisation based data indexing and search system and method. In one embodiment, a computer-implemented method for indexing raw digital data in a searchable format includes translating raw digital data in a first data format to a second data format using a set of extensible parsers, forming non-reductive normalised data entities from the digital data in the second format using a set of extensible entity builders, indexing each of the non-reductive normalised data entities in one or more indexes using a set of extensible indexers, and searching the one or more indexes containing the non-reductive normalised data entities for digital data based on a search query for the digital data.

A student learning guidance platform that includes a functional module for understanding a problem and formulating solutions for the given problem thus formulating and providing possible paths from the given problem to the solutions; a functional module for monitoring how a student solves the problem when the student processes from a first state to a succeeding state in a problem space; a functional module for figuring out whether there is a gap between the student and a required and finding a reason why; a functional module for providing assistance where necessary and further providing a process for a competent teacher to participate assistance when necessary; and a functional module for building a student model and recommending necessary steps for the student to improve.