221 results about "Best fitting" patented technology

Proposed is a mobile automotive car system, and method thereof, for a dynamic, telematics-based connection search engine and telematics data aggregator, wherein risk-transfer profiles are captured and categorized in a results list from a plurality of first risk-transfer systems based on dynamically generated driving score parameters by means of appropriately triggered automotive data. As a variant, during a predefined trial period, the automotive and driving behavior data can be collected, which are transmitted together with the generated driving score parameters to multiple automated first risk-transfer systems for quotation. The user is able to dynamically select the best-fitting first risk-transfer system for risk-transfer by means of the results list, which is provided and updated in real-time for display to and selection by a user of a mobile telecommunication apparatus by means of a mobile telematics application of the mobile telecommunications apparatus.

An apparatus is provided for assessing the location of a drill bit underground. The apparatus includes an acoustic sound generator that is driven by the drilling mud supplied to the drill bit. The sound generator a characteristic string of pulses, which may be termed a signature or key. The key is repeated over and over. Monitors (i.e., sensors) at the surface listen for this key. The key is distorted by the inconstant angular velocity of the drill bit. Thus the observed data do not precisely match the key. On the basis of numerical algorithms, a digitally revised reference signal or key, is identified to map the known reference key onto the best fitting observed data. The correction factors are then applied to map the modified reference key onto the data observed at other sensors of an array of sensors mounted on the surface. By determining the phase shift and travel time of the signals at the various sensors, and having determined the speed of wave propagation in the geological media, the position of the bit, or a fairly close approximation thereof, may be obtained. The correction factors applied to the reference key may also tend to permit the actual rotational speed of the drill bit to be determined.

An algorithm and method to provide personal recommendations for nutrition based on preferences, habits, medical and activity profiles for users, and constraints. The algorithm can also be fed and takes into account real-time feedback from the user. The method allows creating a personal nutritional schedule based on a set of constraints, which are solved using an optimization algorithm to find the diet best fitting each user. The method also includes analyzing a single user by applying various statistical techniques, enabling the algorithm to infer the user's preferences and updating of the constraints, analyzing and clustering of the general user population based on statistical principles, giving the algorithm insightful information and allowing improved performance by means of “machine-learning,” and creating a list of recommended food items/recipes to help users live a balanced, healthier lifestyle.

What is provided in one embodiment is a method and apparatus for confirming that one or more items in a universe of items fall within specified tolerances. In another embodiment various methods for determining best fit between items in a universe of items is provided.

An IT measurement system is aligned with the IT organization's business goals by examining the IT organization's current state-of-affairs with respect to measurement and analyzing how it maps to a proposed measurement model. To ensure the viability of the proposed measurement model, the IT organization and measurement processes it employs are structured to naturally support the collection, analysis, reporting, and use of the measurements in the proposed model. The measurement model and associated techniques work from a top-down approach, starting with the goals of the organization, identifying appropriate behaviors and finally associating measures that will empirically demonstrate achievement of the behaviors. In addition, algorithms for prioritization are executed to ensure that the best fitting measures are selected in the final measurement model.

What is provided in one embodiment is a method and apparatus for confirming that one or more items in a universe of items fall within specified tolerances. In another embodiment various methods for determining best fit between items in a universe of items is provided.

What is disclosed is a novel system and method for determining the number of objects in an IR image obtained using an IR imaging system. In one embodiment, a total of N intensity values are collected for each pixel in an IR image using a IR imaging system comprising an IR detection device and an IR Illuminator. Intensity values are retrieved from a database which have been estimated for a plurality of known materials, such as skin and hair. A classification is determined for each pixel in the IR image using either a best fitting method of a reflectance, or a correlation method. Upon classification, a total number of objects in the IR image can be determined. The present system and method finds its intended uses in of real world applications such as, determining the number of occupants in a vehicle traveling in a HOV/HOT lane.

A method and device to determine quantitatively the surface optical characteristics of an object of reference made up of a plurality of optically differentiable layers. The device includes lighting and image capture means, connected to analysing means which execute the method to obtain the parameters of the repair material components best fitting the optical characteristics of the dental environment of the subject under treatment. The result of the analysis through the method in the invention consists mainly in the identification of each one of the various materials which should constitute the different layers, as well as their respective thicknesses, and which are to lead to a dental repair with optimum optical characteristics. It is thus achieved that the repair be visually imperceptible, both under natural light and under ultraviolet light.

The invention relates to the technical field of oil-and-gas-field geologic modeling, in particular to a shale-gas horizontal-well network crack modeling method based on microseism events. The method includes the steps that 1, data is prepared and imported; 2, a main crack model of shale-gas network cracks is established; 3, in combination with fractured microseism monitoring data, a gridding microseism-event density model and a network crack density model are generated; 4, a branch crack model of the shale-gas network cracks is established; 5, a contact crack model of the shale-gas network cracks is established; 6, the main crack model, the branch crack model and the contact crack model are combined to form a discrete crack model of shale-gas horizontal-well fractured network cracks. According to the shale-gas horizontal-well network crack modeling method based on the microseism events, by establishing the fractured crack models of different levels, the shale-gas horizontal-well complex network crack model is formed, has good fitting effects, and has relatively high practical significance to the development of shale gas.

The invention discloses a method, device, storage medium and terminal device for product defect detections and locations, wherein the method includes: according to the image data characteristics of products to be tested and the product defect complexity, selecting the neural network structure of detection models to be trained; obtaining training data, wherein the training data include training product pictures and standard categories and standard positions of product defects in the training product pictures; according to the training data, training the selected detection models of each neuralnetwork structure; selecting a best fitting detection model from the detection models after training to provide a detection server with the product defect detection, wherein the detection models are used for calculating according to the received product pictures to obtain predicted categories and predicted locations of the product defects in the received product pictures. Adopting the method, device, storage medium and terminal device for product defect detections and locations is capable of improving the accuracy of detecting the product defects.

The invention relates to a biped robot adaptive walking control method based on deep reinforcement learning. The method comprises the steps that 1) a simulation platform is established; 2) a network model based on a deep reinforcement learning method introducing an attention mechanism is constructed; 3) the network model is trained according to the interaction information of a biped robot in the environment of the simulation platform, and the interaction information is stored in a playback pool; and 4) the network model which completes training is used to realize adaptive control of walking of the biped robot. Compared with the prior art, the method provided by the invention has the advantages of fast convergence speed, good fitting effect, high walking stability and the like.

The invention relates to an electrode plate and a lithium ion battery comprising the same. The electrode plate comprises a current collector, an electrode diaphragm and an insulation layer; the electrode diaphragm comprises an electrode active substance; the insulation layer comprises solid insulation padding with the mohs hardness being less than or equal to 5; the insulation layer at least covers an area, which is not covered by the electrode diaphragm, of the surface of the current collector. By adopting the insulation layer, the safety problem caused by the piercing of burrs of the current collector to the diaphragm can be avoided; the insulation layer also has the good extension rate and the good effect on transferring the rolling pressure, the flat appearance of the electrode plate in rolling can be guaranteed, a good fitting interface of the electrode plate inside a cell also can be guaranteed, and the capacity loss of the lithium battery in the charging and discharging processes can be reduced.

An object of the present invention is to provide shoes with good fitting property.

The present invention provides a shoe including: a sole 1 which absorbs landing impact; and an upper 2 which covers an instep of a foot. The upper has a first opening 3 through which the foot is inserted when wearing the shoe. The upper 2 is partly or entirely made of a material which has negative Poisson's ratios.

A web-based application allowing registered MEMBERs to search for and print promotional coupons offered by ADVERTISERs who have the capability to design the terms of such coupons, as well as their graphic design. The relational database management system and software employed allows for registration of users as MEMBERs and ADVERTISERs. It further includes a flexible billing system, allowing users to select a plan best fitting the individual subscriber. Coupon offers are made MEMBER-searchable by an ADVERTISER's company name, type of business (category) and search phrases found in the text of the coupons. Coupons located in the search can be grouped in a virtual “coupon caddy” and subsequently printed out at a MEMBER's workstation computer printer.

The invention discloses a polypropylene melt index predicating method based on a multiple priori knowledge mixed model, which fully explores and utilizes priori knowledge of a polypropylene industrial site, and is used for organically integrating various priori knowledge, embedding the priori knowledge into a multilayer perceptron neural network in a non-linear equality constraint form, and optimizing a network weight number by means of a particle swarm optimization algorithm based on an augmented Lagrange multiplier constraint processing mechanism. Based on the multiple priori knowledge neural network model, the multiple priori knowledge neural network model is organically integrated with a polypropylene melt index simplification mechanism model into a harmonic average mixed soft-measuring model. The multiple priori knowledge mixed soft-measuring modeling method has good fitting prediction ability, and is capable of enhancing model extrapolation capacity and realizing good unity of model extrapolation and prediction accuracy of polypropylene melt indexes. Besides, the method is capable of avoiding zero gain and gain inversion and guaranteeing safety in practical polypropylene melt index quality closed-loop control application.

The invention discloses an optical fiber state evaluating method based on the combination of an improved membership function and a neural network, and relates to the technical field of optical fiber circuit state evaluating and analyzing. The problems that potential failures in a circuit state can not be analyzed and evaluated in the prior art and failures going to happen can not be avoided are solved. By means of the evaluating and analyzing method, the membership weight of an attached optical fiber state membership comprehensive evaluating method is optimized and combined through an improvedEuclidean distance formula, the circuit state healthy degree is accurately evaluated and analyzed, whether potential failures exist in a circuit or not is analyzed, a maintenance strategy is formulated in advance, and the requirement for optical fiber communication ceaseless transmission is met. By means of an RBF neural network evaluating and analyzing model, evaluating performance has good fitting precision and convergence speed, and the evaluating of the optical fiber state is better achieved.

The invention discloses a method for predicting a user participation behavior of a hot topic by an improved RBF (Radical Basis Function) neural network, and belongs to the field of computer network information technology analysis. Firstly, the situation that a neural network can take a good fitting effect for a complex nonlinear relationship among user behaviors is considered, and further a user participation behavior prediction model is built by adopting the RBF neural network; secondly, a mapping relationship between a user attribute and the participation behavior has uncertainty, and a cloud theory is introduced for optimizing an activation function of a hidden layer in an RBF; and finally, topic popularity is subjected to exponential function model-based parameter fitting by utilizing time discretization and time slicing methods for a characteristic that the participation behavior of a user is changed with time, so that a topic popularity change trend is obtained.

A method and system allowing the ability to automatically and systematically run thousands and even millions of combinations and permutations of regression, forecasting and econometric trials to determine the best-fitting predictive model.