51 results about "Minimum risk" patented technology

A method and system are provided that minimize wafer or package level test time without adversely impacting yields in downstream manufacturing processes or degrading outgoing quality levels. The method provides optimization by determining, a priority, the most effective set of tests for a given lot or wafer. The invention implements a method using a processor-based system involving the integration of multiple sources of data that include: historical and realtime, product specific and lot specific, from wafer fabrication data (i.e., process measurements, defect inspections, and parametric testing), product qualification test results, physical failure analysis results and manufacturing functional test results. These various forms of data are used to determine an optimal set of tests to run using a test application sequence, on a given product to optimize test time with minimum risk to yield or product quality.

A method and system are provided that minimize wafer or package level test time without adversely impacting yields in downstream manufacturing processes or degrading outgoing quality levels. The method provides optimization by determining, a priority, the most effective set of tests for a given lot or wafer. The invention implements a method using a processor-based system involving the integration of multiple sources of data that include: historical and realtime, product specific and lot specific, from wafer fabrication data (i.e., process measurements, defect inspections, and parametric testing), product qualification test results, physical failure analysis results and manufacturing functional test results. These various forms of data are used to determine an optimal set of tests to run using a test application sequence, on a given product to optimize test time with minimum risk to yield or product quality.

The invention discloses a fault diagnosis method during the industrial process. The method comprises the steps of collecting historical normal data during the industrial process; calculating a detection statistics based on the historical normal data during the industrial process; collecting the to-be-detected data of the industrial process; on the condition that the industrial process is detected to be out of order, extracting a statistic feature based on the relative refactoring contribution method; according to the statistic feature, calculating a conditional probability density function in the fault mode and a conditional probability density function in the normal mode; according to the prior probability and the conditional probability density function, calculating a posterior probability; conducting the fault variable recognition on a current time sample based on the minimum risk Bayesian decision theory; according to a diagnosis result, updating the prior probability for the next time sample and conducting the fault diagnosis and recognition again for the next round. According to the technical scheme of the invention, the major failure variable, the secondary process variable and the normal variable of the current sample are distinguished. Meanwhile, the diagnosis result of the process variable of the previous time sample is applied to the diagnosis of the current sample. Therefore, the pollution effect during the fault diagnosis of the industrial process is eliminated.

A financial planner and portfolio simulation system projects the simulated cumulative risks and rewards of a plurality of mixed portfolios in relation to the expected risk and reward of a minimum-risk or relatively risk-free baseline portfolio. Through simulation, the system generates, for each mixed portfolio, a distribution of average retirement budgets that the corresponding mixed portfolio would be projected to sustain. An asset allocation risk and reward spectrum chart is provided that graphs various percentile-ranked samples from each distribution on a computer-generated plot that also displays the sustainable retirement budget supported by a baseline portfolio. The statistical and graphical comparisons of the mixed portfolios to the baseline portfolio assist investors in choosing mixed portfolios best suited to their retirement goals and risk tolerance.

An automated asset based lending (ABL) system that integrates supplier receivables data, allowing financial institutions optimally to value and price lending against those receivables, thus enabling financial institutions to offer suppliers a maximum borrowing base with minimum risk and highest return to the financial institution, further enabling the financial institution to conduct business in multiple currencies, time zones and countries using a computer based ABL process. In an asset based lending system having a client and at least one financial institution, a method for valuing and pricing client's receivables, the method comprising, downloading accounts receivable information from the client, the accounts receivable information having a specified currency, calculating a borrowing base for the client via utilizing debtor risk ratings, pricing profiles, valuation profiles and pricing tiers, and applying fees and interest as specified in the pricing profile associated with the valuation profile.

Pesticidal compositions which are environmentally safe, consisting essentially of a minimum risk pesticide in combination with one or more inert ingredients are disclosed.

The invention discloses a Bayesian algorithm-based content filtering method. Content filtering is performed for text information in a 3rd generation mobile communication core network, text classification is performed by using a double threshold-based Bayesian algorithm, C1 is set to be normal information, C2 is set to be junk information, a classifier estimates the probability that a characteristic vector X which represents a data sample belongs to each class Ci, and a Bayesian formula for the estimation is that: P(Ci / X) = P(X / Ci) P(Ci) / P(X), wherein i is more than or equal to 1 and less than or equal to 2, the maximum value of a posterior probability is called the maximum posterior probability, for an error (a reference source is not found) of each class, the error (a reference source is not found) only needs to be calculated, a characteristic vector X of an unknown sample is assigned to the Ci class of the error (a reference source is not found) with the minimum risk value. Characteristic selection is performed by adopting document frequency (DF), and classification is performed by using minimum risk-based double threshold Bayesian decision. In a time division-synchronous code division multiple access (TD-SCDMA) mobile internet content monitoring system, the algorithm has higher controllability and can realize real-time high-efficiency classification of mass text information.

The invention discloses a fast method for HEVC (High Efficiency Video Coding) block size partition based on a Bayes decision. The fast method comprises the following steps: first of all, dividing a video sequence into an online learning stage and a fast partitioning stage by employing scene change detection based on an average gray scale difference; then, for the online learning stage and a video frame which occurs a scene change, in each partitioning depth, respectively extracting Jinter and Jintra of a CU (Coding Unit) as characteristic values, thereby establishing a mixed Gaussian model, wherein specific parameters of the model are determined according to an EM algorithm initialized by a K-Means algorithm; and for a to-be-partitioned CU in the fast partitioning stage, extracting the characteristic values and finding a conditional probability on whether to partition according to the mixed Gaussian model, and at last, finding the decision with a relatively small risk by employing a Bayes formula of a minimum risk to take as a judgment basis on whether the current CU is partitioned. According to the fast method disclosed by the invention, the algorithm complexity is reduced, and the coding time can be greatly reduced.

A cable management system and method of use integrated with the cable comprising, in general, a coiled cable; a bendable elongated member positioned on one section of the cable; an attachment means to affix the elongated member to the cable; wherein the bendable elongated member integrated with the one section of the cable is configured to wrap around the coiled cable and, thus, functions to maintain the coiled cable in an organized manner ready for a subsequent use with minimum risk of cord entanglement.

The invention relates to an oil and gas trap combination exploration optimization method. According to the optimization method, an optimization theory in operational research is used; on the basis that reasonable geological reserve calculation and strict risk analysis are carried out on a trap combination, an oil and gas trap linear programming model is established; under the conditions of capital constraint and a determined risk scope, the maximum geological reserve of the oil and gas trap combination is acquired; or under the conditions of capital constraint and a determined geological reserve condition, the minimum risk variance of the oil and gas trap combination is acquired; and balance between risk and return of the whole oil and gas trap combination exploration is realized. Practical application shows that the method has the advantages of reliable effect and strong operability, can provide a scientific basis for the decision of oil and gas trap exploration, and has strong practicality.

A fumigant pesticide composition made up of a base formula and enhancement formula, which include botanical essential oil-based active ingredients and inert materials identified under the United States Environmental Protection Agency's approved list of minimum risk pesticides. The enhancement formula is effective to inhibit or enhance the release of the secondary metabolites of the essential oil-based active ingredients. An apparatus and method for the controlled release of the fumigant pesticide composition is also disclosed.

The purpose of the present invention is to provide methods for producing recombinant polyclonal immunoglobulins using recombinant DNA techniques, which provides constant supply of immunoglobulin preparations with minimum risk of infection. A method of the present invention comprises the following steps: (1) isolating a plurality of types of genes from cDNAs derived from tissues or cells expressing immunoglobulins, said genes encoding a plurality of types of polypeptides respectively, and said polypeptides containing a plurality of types of immunoglobulin variable regions respectively, and preparing mixture of the genes; (2) preparing a plurality of types of recombinant vectors into which a plurality of types of genes are introduced respectively by contacting said mixture of the genes with vectors, and preparing mixture of the recombinant vectors; (3) preparing a plurality of types of transformants into which a plurality of types of recombinant vectors are introduced respectively by contacting said mixture of the recombinant vectors with host cells, and preparing mixture of the transformants; and (4) culturing said mixture of the transformants, and obtaining mixture of polypeptides from the transformants culture, wherein said polypeptides contain a plurality of types of immunoglobulin variable regions respectively.

Methods of preventing or inhibiting putrefaction, degradation, and / or deterioration of a surface of wet animal hide or skin are described. The method can include applying a composition containing at least one minimum risk fungicide to the surface or to a solid, liquid or gas that comes in contact with the surface. Treated hides or skin are further described.

The present invention relates to a composition that involves a dextrin and one or more plant treatment agents. In this composition, the dextrin and one or more plant treatment agents interact such that some of the dextrin sequesters the one or more plant treatment agents, some of the dextrin is attached to the one or more plant treatment agents, and some of the dextrin is mixed with, but unattached to, the one or more plant treatment agents. The present invention also relates to a method that involves providing a dextrin and providing one or more plant treatment agents.

The invention discloses an intelligent investment system data processing method based on massive financial data. The method comprises the steps that historical data of financial product transaction are saved in a database; the historical data sequences are smoothly processed; result statistics is performed on the processed historical data by using a chart analysis module so that a corresponding chart of the type of the invested financial products, investment amount and its benefits is obtained; a benefit model of the financial products is constructed according to the type of the selected financial products and the investment amount; the constructed model is operated; and simulation software obtains the type of the bought financial products and the maximum benefit amount according to the constructed model. The accurate benefit value is obtained according to the constructed model and the calculating result of the simulation software so that rigidity of performing financial analysis and prediction through purely considering the historical data can be avoided, and the maximum benefit and the minimum risk are ensured to be obtained in the transaction process for users.

The quantitative construction of investment portfolios of securities such as stocks, bonds, or the like using optimization is addressed. More specifically, during optimization, constraints on non-target factor exposures are automatically converted to constraints on the exposure of the projections of the non-target factors that are orthogonal to a specified target factor. The target factor may be the implied alpha of a reference portfolio, such as a traditional minimum risk portfolio. Such constraints may be utilized to produce portfolios with superior performance to those produced with traditional factor exposure constraints.

The invention discloses a method for selecting an ideal cloud service provider. The method is implemented on the basis of a SelCSP framework, and the method comprises the following steps: establishing a set of standard SLA parameters for each cloud service provider; obtaining related information of the SLA parameters of each cloud service provider, summarizing the transparency of all SLA parameters, and using an average value as the overall ability of the cloud service provider on the SLA; calculating the credibility of each cloud service provider according to the interaction log condition indicating whether a client has a direct interactive relationship with the cloud service provider in the environment in the past and the credibility grade and the feedback grade provided by the client; calculating an overall interaction risk value of each cloud service provider according to the ability and the credibility of the cloud service provider; and pushing the overall interaction risk value of each cloud service provider to the client. The method has the advantages that the ideal cloud service provider having the minimum risk value can be selected according to the risk evaluation results of the cloud service providers and can be pushed to the user.

The invention relates to a collision avoidance decision-making method based on a water surface unmanned ship. The collision avoidance decision-making method comprises the following steps: detecting and collecting each target in real time by using a sensing system of the water surface unmanned ship; analyzing the meeting situation, and judging whether a collision risk exists or not; calculating feasible collision avoidance measures for each target with the collision danger; fusing the similar collision avoidance measures, and obtaining the optimal solution of the various collision avoidance measures; establishing a collision avoidance decision risk assessment model, and calculating the risk values of the optimal solutions of various collision avoidance measures; and selecting an optimal collision avoidance scheme according to the calculated risk value, outputting the optimal collision avoidance scheme to an execution system, and updating in real time until the collision avoidance process is completed. The collision avoidance decision-making method is reasonable in design, and can carry out risk coefficient resolving and comparison on various feasible collision avoidance action schemes in real time, so as to obtain a collision avoidance decision scheme with the minimum risk coefficient and provide the collision avoidance decision scheme for the water surface unmanned ship, so that the rapid and safe obstacle avoidance function of the unmanned ship is achieved, and navigation safety of the unmanned ship is guaranteed, and the collision avoidance decision-making method is simple and easy to achieve.

Computer-implemented systems and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located. The systems and methods transform risk parameter data into risk metrics that allow comparison of relative risk between suppliers, supplier sites, or geographic locations, and allow comparison of risk metrics to minimum risk scores calculated for a given metric. The systems and methods further provide guidance / proposed action to take based on the generated risk metrics.

The invention discloses an abnormal user detection method based on the minimum risk of a deep neural network. The method comprises firstly, preprocessing data of an abnormal user to obtain data of theabnormal user with the same amount of data of a normal user; secondly, using a Mini-batch gradient descent method to train a deep neural network and combining with the degree of harm of different abnormal users in an NOMA communication system to establish a decision table based on the minimum risk; and finally, introducing loss functions corresponding to the different decisions set by the decision table into the deep neural network to construct an abnormal user detection method based on the minimum risk of the neural network. The method solves the problem of NOMA system abnormal user detection based on big data through strong expression and analysis capabilities of the deep neural network to high-dimensional data, solves the problem of differences in the degree of harm of various abnormalusers in the NOMA system by introducing the minimum risk in the deep neural network, and provides new ideas and theoretical innovations for abnormal user detection.

The invention discloses a device for a steel cover box with a bottom of a deep water high-rise pile cap by a hoop-embracing method and a construction method. The device comprises a plurality of concrete tubular piles, hoops arranged on the concrete tubular piles and the steel cover box connected to the hoops, wherein the steel cover box comprises a bottom plate and a side plate fixedly connected to the bottom plate; the bottom plate comprises a plurality of bottom modules, a bottom sealing plate fixedly connected to the bottom modules and concrete on the bottom sealing plate; the bottom modules are fixedly connected to the hoops; each of the hoops comprises two semi-circular steel plates which are fixedly connected to the concrete tubular piles through high-strength bolts; corbels are arranged on the semi-circular steel plates; and the bottom modules are fixedly connected to the corbels. According to the device disclosed by the invention, the quality and safety stability of a steel cover box technology are guaranteed, so that a project is constructed with a relatively low cost and the minimum risk, the problem that the steel cover box with the bottom is free of supporting points on the concrete tubular piles is solved, the problem that the steel cover box which is relatively large in planar dimension is hard to hoist integrally is solved, and the application range of a deep water high-rise pile cap construction technology is expanded.

The invention discloses a method for obtaining a reservoir group multi-objective scheduling risk analysis optimal equilibrium solution. The method comprises the acquisition of a multi-objective scheduling risk analysis noninferior solution, the calculation of a uniformity rate decision method of multi-objective scheduling risk analysis, and the calculation of a risk and benefit conversion relation and a threshold determination process in multi-objective scheduling. In the acquisition of the multi-objective scheduling risk analysis noninferior solution, firstly a cascade reservoir combined dispatching multi-objective minimum risk model is established, then a scheme is obtained through a selection operation in a genetic algorithm to carry out non-dominated sorting, and through the continuous renewal and elimination of crossover and variation operations in the algorithm, finally a solution curve or curve surface formed by a solution set is outputted. In the calculation process of the uniformity rate decision method of the multi-objective scheduling risk analysis, and based on a uniformity rate, a decision mode of the optimal equilibrium solution is determined. In the process of the calculation of a risk and benefit mutual conversion relation and a threshold determination process in multi-objective scheduling, based on the bifurcation theory, the threshold of a risk and benefit conversion relation between objectives is analyzed and determined.

The invention provides an urban rail transit road network safety assessment and enhancement method based on passenger flow distribution, and the method employs a risk function to quantitatively assessthe safety of a road network, and then carries out the passenger flow distribution at a road network level, so as to reduce the risk. According to the method, the matching degree of the actual operation states and the capacities of intervals and stations is used as the input of a risk function, and the static statistical indexes of the stations and the intervals in a road network are used as factors to carry out weighted summation on risks, so that the importance of different stations and intervals is embodied, and the overall risk of the road network is obtained through calculation. The minimum risk is used as an optimization target, constraint conditions are analyzed, it can be found that the optimization problem is a convex optimization problem, and therefore the optimal flow distribution scheme of the road network can be obtained. The method provided by the invention can enable the transport capacity to be more matched and the road network operation state to be more stable, thereby reducing the risk and improving the safety.

The invention discloses a rumor risk assessment method based on A network risk entropy difference. The method comprises: S1, establishing an SIR model based rumor propagation model for a network platform to be assessed; S2, using the rumor source identification method to identify a rumor source and the propagation time on the basis of a network structure of the rumor propagation model; S3, simulating a forward propagation process of the rumor in the network structure to obtain the probability that each node in the network is in a different state at the current time; S4, calculating the maximumrisk entropy and the minimum risk entropy of the network at the current time according to the probability that each node is in a different state at the current time; S5, calculating the network riskentropy of the current time by using the maximum risk entropy and the minimum risk entropy of the network at the current time; and S6, calculating the network risk entropy difference according to thenetwork risk entropy at the current time and the previous time of the network, and assessing the potential risk caused by the rumors at the current time to the network platform according to the network risk entropy difference. The invention can quantitatively and accurately assess the risk caused by rumors to the network in real time.

Methods of preventing or inhibiting putrefaction, degradation, and / or deterioration of a surface of wet animal hide or skin are described. The method can include applying a composition containing at least one minimum risk fungicide to the surface or to a solid, liquid or gas that comes in contact with the surface. Treated hides or skin are further described.

The invention discloses an intermittent process mode recognition method based on Bayesian statistical analysis, and belongs to the technical field of intermittent process monitoring. The method comprises the steps of firstly, expanding three-dimensional historical process data of an intermittent process into two-dimensional data along a batch method, and carrying out data standardization on the expanded data; secondly, performing clustering analysis on the standardized process data by utilizing a fuzzy C-mean clustering algorithm, setting a mode coarse division subordination rule, and obtaining a mode coarse division result; and finally, analyzing the mode coarse division result by utilizing a Bayesian network classifier, introducing a mode inference coefficient of a time sequence constraint, inferring a minimum risk criterion according to a mode, and judging final attribution of the mode, thereby realizing mode recognition of the intermittent process. According to the method, the timesequence constraint of intermittent process data is fully considered; effective division of a stable mode and a transition mode of the intermittent process is achieved through the Bayesian statistical analysis; and the method has relatively high mode recognition accuracy.

The invention provides a machine reading understanding model training method and device based on a joint loss function. Specifically, when machine reading understanding model training is carried out,a loss function composed of a maximum likelihood estimation function and a minimum risk training function is used as an evaluation index of a machine reading understanding model so as to guide adjustment of parameters of the machine reading understanding model. The idea of the minimum risk training function is to use the loss function to describe the difference degree between the answer output bythe model and the standard answer. The maximum likelihood estimation function, namely loss, tries to find a group of model parameters, so that the loss value of the machine reading understanding modelon the training set is minimum, and therefore, compared with the mode of singly utilizing the maximum likelihood estimation function, the model trained by the method provided by the invention can extract answers more accurately.