10 results about "Numerical system" patented technology

A development system for predictive diagnostics is provided. During operation, the system can perform a first diagnostic test on a distributed computing system based on a first restriction level indicating resource consumption of a first set of hardware units. The distributed computing system can include a plurality of computing devices with processing and memory resources. The system can generate a first log comprising a first set of parameter values indicating an output of the first diagnostic test at the first restriction level of the distributed computing system. The system can configure a first diagnostic tool with the first set of parameter values to emulate the first diagnostic test. The system can then apply the first diagnostic tool to obtain a second set of parameter values indicating an output of the first diagnostic test at a second restriction level, which can be higher than the first restriction level.

A system for ophthalmic imaging comprising an ophthalmic device configured to obtain stereoscopic images of an eye of a patient and to transmit the images in real-time to a display device via a network for viewing by practitioners. The ophthalmic device comprises at least an optic assembly, a processing assembly, a slit assembly, such as a slit lamp, and a positioning assembly. Control devices structured to control the ophthalmic device over the network, such as the world wide web, can be disposed at a plurality of locations, and may be remote from the ophthalmic device while providing real time control of the parameters of the ophthalmic device by the practitioner(s) associated therewith.

Method for the automated calibration of functions of a driver assistance system (Advanced Driver Assistance System ADAS / Automated Driving System, ADS) for a variety of scenarios and vehicle configurations, comprising: - Training (S10) a learning reinforcement agent (410) of an optimization module (400) with a training data set (240), wherein the optimization module (400) comprises at least the learning reinforcement agent (410), an action module (420), an environment module (430), a state module (440) and a reward module (450), and wherein the training data set (240) contains calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) of an ADAS / ADS system (10); - generating (S20) a strategy of the learning reinforcement agent (410) for varying calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) in the form of actions (A i) for optimising calibration data sets (15) of ADAS / ADS systems (10); - Entering (S30) a calibration data set (15) of the ADAS / ADS system (10) with calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) via a user interface (220) of an input module (200) or from a database (300); - Selecting (S40) a large number of test cases (T i ) from the database (300) and generating simulation environments with simulation scenarios from the test cases (T i ), where a test case (T i ) to select a parameterized scenario (SZp i ), where both scenario parameters (P i ) as well as associated scenario parameter values ​​(PV i ) not all are fixed, concrete scenario parameters (Pc i ) and concrete scenario parameter values ​​(PVc i) and further information for simulating the behavior of the ADAS / ADS system (10) in a specific driving situation and for managing a specific driving task; carrying out (S50) simulations of the behavior of the ADAS / ADS system (10) in the simulation scenarios, namely in concrete scenarios (SZc i ), in which the concrete scenario parameters (Pc i ) and the corresponding concrete scenario parameter values ​​(PVc i ) or value ranges of the concrete scenario parameter values ​​(PVc i ) and generating simulation results, wherein the simulation results reflect the system behavior of the ADAS / ADS system (10) in the simulation scenarios; - Evaluate (S60) the simulation results in the form of key performance indicators (KPI i ) which describe the behavior of the ADAS / ADS system (10) in the numerous test cases (T i ) defined simulated concrete scenarios (SZc i) and performance values; - Determining (S70) states (S i ) from the key performance indicators (KPI i ) and rewards (R i ) from the performance values; - Calculate (S80) modeled calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) to achieve improved system behavior of the ADAS / ADS system (10) in the simulation scenarios by the trained learning reinforcement agent (410) in several iterations until an optimized calibration data set (17) has been generated; - generating and outputting (S90) output results (550) by an output module (500), wherein the output results (550) contain the optimized calibration data set (17).

The invention relates to the technical field of data center management, in particular to an intelligent optimization control system for data center energy management, which realizes cross-domain dynamic linkage by constructing energy, network and business three-side collaborative twinbodies, balances energy and computing power resources while guaranteeing business service levels, avoids global loss of local optimization, and improves the service quality. By using the twinborn rehearsal technology, the system can simulate the whole-process influence of different optimization strategies in a virtual space, and through a cooperative fault processing mechanism of a service and network side twinborn body, the system constructs a'fault early warning-load transfer-energy consumption compensation 'closed loop, thereby greatly reducing the risk of core service interruption, and improving the reliability of the system. The system toughness and the stable operation capability of optimization control in an extreme scene are enhanced, a contribution degree, reputation and benefit evaluation method is provided, an abstract optimization effect is converted into a comparable numerical system, an objective and explainable decision basis is provided for selection of an optimization control strategy, and subjective deviation driven by experience is avoided.

Method for the automated calibration of functions of a driver assistance system (Advanced Driver Assistance System ADAS / Automated Driving System, ADS) for a variety of scenarios and vehicle configurations, comprising: - Training (S10) a learning reinforcement agent (410) of an optimization module (400) with a training data set (240), wherein the optimization module (400) comprises at least the learning reinforcement agent (410), an action module (420) with an action space, an environment module (430), a state module (440) and a reward module (450), and wherein the training data set (240) contains calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) of an ADAS / ADS system (10); - generating (S20) a strategy of the learning reinforcement agent (410) for varying calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) in the form of actions (A i) for optimising calibration data sets (15) of ADAS / ADS systems (10); - Entering (S30) a calibration data set (15) of the ADAS / ADS system (10) with calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) via a user interface (220) of an input module (200) or from a database (300); - Selecting (S40) a large number of test cases (T i ) from the database (300) and generating simulation environments with simulation scenarios from the test cases (T i ) in the environment module (430); - performing (S50) simulations of the behavior of the ADAS / ADS system (10) in the simulation scenarios and generating simulation results, wherein the simulation results reflect the system behavior of the ADAS / ADS system (10) in the simulation scenarios; - Evaluate (S60) the simulation results in the form of key performance indicators (KPI i ) and performance values; - Determining (S70) states (S i ) from the key performance indicators (KPI i ) and rewards (R i ) from the performance values; - Calculate (S80) modeled calibration parameters (Pcal i ) and calibration parameter values ​​(PVcal i ) to achieve an improved system behavior of the ADAS / ADS system (10) in the simulation scenarios by the trained learning reinforcement agent (410) in several iterations until an optimized calibration data set (17) has been generated, wherein to determine the decisions of the learning reinforcement agent (410) regarding the actions to be carried out (A i ) the action space of the action module (420) into discrete parameter step sizes with which a certain parameter (P i) during which the optimization is adapted or updated, is divided such that, in addition to a first parameter step size, which is predetermined by the discretization of the action space, a second parameter step size is added, the amount of which is smaller than the amount of the first parameter step size, and wherein the learning reinforcement agent (410) determines the calibration parameter value (PVcal i ) of a calibration parameter to be varied (Pcal i ) by one or more parameter increments, such that the learning reinforcement agent (410) decides whether to use a value of a calibration parameter (Pcal i ) by a certain number of the given first step size of the action area and by a certain number of the smaller second step size by adding, subtracting or leaving the first and second step sizes unchanged; - generating and outputting (S90) output results (550) by an output module (500), wherein the output results (550) contain the optimized calibration data set (17).

Technical solutions are directed to an intersystem configuration adjustment for periodic operation processing. A processor can receive, from an entity system, a value for a parameter of a client account. The system can determine, based on a configuration for an entity associated with the client account, to perform parameter adjustment according to a protocol of the configuration and identify a rule for parameter adjustment. The system can split, using the rule and based on an accumulated parameter value, the value for the parameter into component values. The system can assign, according to ranking of modifiers, a modifier to each of the components values and generate an adjusted parameter value. The system can command the processing system to execute a process for the client account and the time interval based on the adjusted parameter value.

The present invention discloses a method and system for constructing a vectorized Jacobian matrix based on a node voltage equation, and relates to the field of power systems. Currently, the construction of the Jacobian matrix requires a large amount of calculation and has poor applicability. The present invention comprises the steps of: establishing a corresponding system model according to the target power system structure, establishing a system admittance matrix and a node voltage equation in combination with system parameters; then, mathematically deducing based on the system node voltage equation to obtain the system flow Jacobian matrix structure, and realizing vectorization during the construction process; finally, calculating the system flow by the Newton-Raphson method, substituting the calculated results, and obtaining the numerical system Jacobian matrix under the corresponding scenario. This technical solution proposes a method for constructing a flow Jacobian matrix that realizes process vectorization through mathematical derivation based on the node voltage equation, thereby providing a new practical method for constructing the flow Jacobian matrix.

The present application relates to the technical field of data center management, and in particular to an intelligent optimization control system for data center energy management, which realizes cross-domain dynamic linkage by constructing energy, network and business three-side collaborative twins, balances energy and computing power resources while ensuring business service level, avoids global loss of local optimization, uses twin pre-play technology, the system can simulate the whole process influence of different optimization strategies in virtual space, through the cooperative fault handling mechanism of business and network side twins, the system constructs a closed loop of "fault early warning-load transfer-energy compensation", greatly reduces the risk of core business interruption, strengthens the system resilience and stable operation ability of optimization control in extreme scenarios, the contribution, reputation and benefit evaluation method proposed converts abstract optimization effect into a comparable numerical system, providing objective and interpretable decision basis for optimization control strategy selection, avoiding subjective bias driven by experience.

The present invention relates to introductory teaching timepieces in general and specifically to a novel design applicable to watches and clocks whereby only the second hand is present and from which the minute and hour hands have been removed. The second hand moves in equally timed units such that one full cycle of its motion consists of 60 equally timed seconds. In doing so, it isolates the sexagesimal basis for time-telling so that the beginner can understand this numerical system prior to combining it with the duodecimal system used for minutes and hours.

This invention belongs to the field of internet data processing and game service technology. The unified incentive platform is primarily displayed independently within each game via an embedded interface, and can also be promoted and displayed through external channels such as short video platforms and social media platforms. Channel promotion costs are covered by advertising incentive revenue. Users enter the platform through in-game or external user-friendly entry points, complete identity verification, and bind at least one game account from a different operating entity. After a user fully watches an advertisement and passes multiple verifications, the platform immediately generates a limited-time revenue boost and simultaneously starts the timer. The correspondence between individual advertisement duration, number of ad plays, timer T, and revenue boost ratio is dynamically adjusted by the advertising operator based on market feedback, with a basic timer T range of 6 to 48 hours. The boost is only a qualification for real-time revenue increase and does not correspond to independent virtual items, currency, or props; it only applies to basic real-time revenue generated by users through manual interaction to complete tasks and defeat monsters, and has no effect on non-interactive revenue such as recharges, system gifts, and event rewards. The virtual items, currency, and revenue obtained through the boost are subject to the original usage, circulation, and consumption rules of the corresponding game, without adding new item types or altering the original game's numerical system. Permissions are valid for a period of T. They are automatically reset and permanently expire upon expiration, and are not stored in the inventory, reissued, or extended. The bonus takes effect after the user logs into the game within the validity period and passes server verification. Basic benefits are increased proportionally, and the status and remaining validity are displayed in real-time.