499 results about "Flow time" patented technology

A system to detect anomalies in internet protocol (IP) flows uses a set of machine-learning (ML) rules that can be applied in real time at the IP flow level. A communication network has a large number of routers that can be equipped with flow monitoring capability. A flow collector collects flow data from the routers throughout the communication network and provides them to a flow classifier. At the same time, a limited number of locations in the network monitor data packets and generate alerts based on packet data properties. The packet alerts and the flow data are provided to a machine learning system that detects correlations between the packet-based alerts and the flow data to thereby generate a series of flow-level alerts. These rules are provided to the flow time classifier. Over time, the new packet alerts and flow data are used to provide updated rules generated by the machine learning system.

Microfluidic devices, systems, and methods measure viscosity, flow times, and/or pressures, other flow characteristics within the channels, and the measured flow characteristics can be used to generate a desired flow. Multi-reservoir pressure modulator and pressure controller systems, electrokinetic systems and/or other fluid transport mechanisms can generate the flow, controllably mix fluids, and the like.

A method to model a complex system of pipes. The model takes into account the physical processes in a tree-type piping system and provides for an accurate modeling of a real world tree-type piping system. In a preferred embodiment, a computer program is provided for analyzing models of dry pipe systems. The computer program includes a user interface and a computational engine. The user interface allows a model of a dry pipe system to be defined and the computational engine determines a liquid flow time through the model of the dry pipe system. The computational engine provides a verification of the liquid flow time in a model of a referential dry pipe system within 20% of an actual liquid flow time in the referential dry pipe system.

The invention relates to a vehicle flow predicting method based on an integrated LSTM neural network. On the basis of historical data obtained by vehicle flow detection, an integrated LSTM neural network vehicle flow prediction model is established to carry out vehicle flow prediction, so that the generalization error of the prediction model is reduced and the accuracy is improved. The method comprises the following steps that: data preprocessing is carried out; according to a preprocessed vehicle flow time sequence value, a vehicle flow matrix data set is constructed and the vehicle flow of an (n+1)th period of time is predicted by using first n periods of time, wherein each period of time is delta t expressing the time length and the unit is min; a plurality of different LSTM neural network models are constructed by using different initial weights; on the basis of a bagging integrated learning method, a training set and a verification set are constructed; a plurality of LSTM neural networks are trained to obtain an optimized module; a weighting coefficient of the single LSTM model is calculated by using the verification set; and inverse transformation and reverse normalization are carried out on a predicted vehicle flow value to obtain a predicted vehicle flow and integrated weighting is carried out to obtain a vehicle flow value predicted finally by the model.

A method for determining where in time to schedule a project in a computerized system for scheduling multiple projects operates in conjunction with a database composed of schedules for individual projects with resources assigned to some tasks. A user specifies the resources to be used for a new project, the loading caused by projects whose dates are already determined, the earliest possible start times for the new project, and the maximum amount of expansion in flow time (start to finish time) allowed for the new project, with considering inter-project resource contention. The new project is delayed only long enough to allow contention for the selected resources to be resolved, while not violating the flow time expansion restriction.

A system and method for efficiently and continuously allowing vehicles to travel through an intersection. The method includes broadcasting a synchronization signal to all vehicles that will be entering the intersection and broadcasting an intersection flow time to all of the vehicles that will be entering the intersection that identifies which travel lanes travel in what direction. The method also includes identifying an arrival synchronization pattern for all of the vehicles that will be entering the intersection and controlling a speed of the vehicles traveling through the intersection and a time for the vehicles entering the intersection so that vehicles traveling in perpendicular or cross directions to the intersection will simultaneously travel through the intersection without colliding with each other.

The invention discloses a traffic flow predicting method based on a Conv1D-LSTM neural network structure. The method includes the following steps of firstly, preprocessing road traffic flow data and establishing a road traffic flow state matrix data set; secondly, obtaining traffic flow states of different road sections at the same moment and extracting the space features of the traffic flow data;thirdly, extracting data time features on the basis of the traffic flow data including the space features, wherein the traffic flow space features output by a one-dimensional convolution network serves as the input of an LSTM neutral network, and the time features in the road traffic flow data are further extracted; fourthly, using road tariff flow time and space features as the input of a regression predicting layer, calculating a predicting result corresponding to the current input, defining a model loss function, continuously optimizing model parameters through a counterpropagation algorithm according to the value of the loss function, and obtaining real-time traffic flow data to serve as model input for predicting the real-time road traffic flow. By means of the method, the traffic flow short-period predicting accuracy is improved.

The invention relates to a method for preparing a thermoelectric material of a ternary diamond structure. The method comprises the steps of: raw material preparation, heat-preservation reaction, annealing treatment, spark plasma sintering and the like. In the invention, by applying a fusion-process preparation technology, rapid spark plasma sintering and other methods and controlling technological parameters, the method for preparing the thermoelectric material of the ternary diamond structure is realized and has the advantages of simple and convenient technology, short flow time, high feasibility and good thermoelectric performance, and the thermoelectric material of the ternary diamond structure, prepared with the method provided by the invention, has favorable thermoelectric performance and can be used as a p-type thermoelectric material. The method has the characteristics of simplicity, strong operability, low cost, good industrial prospect and the like.

The invention provides a cloud cluster extraction method of network information. The cloud cluster extraction method comprises the following steps of: performing file writing, data storage and access to network information by a distributed file system; performing seamless combination on calculation models Map/Reduce of SOM (Self-Organizing Maps), a Kmeans clustering algorithm and cloud calculation to obtain a Map/Reduce SOM and Kmeans clustering algorithm based on the cloud calculation; performing control on the whole Map/Reduce by JobTracker, and distributing Map tasks or Reduce tasks by free TaskTracker; executing an instruction sent from the JobTracker and processing movement of data between Map and Reduce phases at the same time by the TaskTracker; periodically reporting finished work and state updating by each TaskTracker node; and if one TaskTracker node keeps silent for longer than a pre-set time interval, recording that the state of the node is dead and sending data distributed to the node to the other nodes by the JobTracker. The cloud cluster extraction method of the network information has good characteristic extracting performance and overcomes the disadvantage of too strong subjectivity in the existing network flow time sequence analyzing and predicating algorithm.

A system and method for facilitating delivery of fuel to a customer site with a delivery truck. A hand-held device captures an identifier specific to the given customer. A zero flow of fuel being transferred from the delivery truck to a customer storage tank is sensed, and the fuel delivery is tagged for billing to the customer based upon when the zero flow is sensed in relation to when the identifier is captured. The hand-held device is used to log the identifier and an identification time at which the identifier is captured. A computer is provided on-board the delivery truck, and logs zero flow indications and the respective zero flow times. The system determines which ones of the zero flow times are after the identification time but before a next occurring identification time at which the hand-held device captures a next identifier.

An embodiment of the invention provides a method for performing alarm locating on batch complains and an alarm locating device. The method comprises the steps of acquiring complain information in real time, abstracting key fields from the complain information to serve as relative information points; abstracting signaling failure information from a signaling monitoring system according to the complain information and establishing a service failure information base; scanning the relative information points and the service failure information base and finding failure signaling in specified time; and inputting the failure signaling into an intelligent relative rule trigger for detection, and when the failure signaling accords with a threshold set in the intelligent relative rule trigger, generating alarm information and triggering alarm. The method for performing alarm locating on batch complains and the alarm locating device improve timeliness ratio and locating accuracy of great batch complains, reduce manual judgment links of customer service and network maintenance personnel when faults occur, and reduces information distortion and flow time.

The invention belongs to the technical field of data processing, and provides a method and a system for statistics and process of tourist density distribution. Scenic spots and the tourist density distribution in a scenic spot are counted automatically by obtaining flow time, view and admire time, view and admire position and state information of tourists in the scenic spot are obtained. Circulating speed and direction of visit of the tourists are predicted through a density distribution prediction model so as to provide scientific basis for the scenic spots or visiting sequence of the tourists, and bad playing quality of the tourists caused by crowd is avoided. Tourism resources can be distributed reasonably through real-time distribution of tourist density and split-flow and guidance with proper measures, the tourist environment is further protected, damages of tourist activities to the environment and facilities are reduced, management ability of the scenic spots is enhanced, the product quality safety level of tourism products is improved, sound development of tourism industry is promoted, and the method and the system have great significance on personalized service of the scenic spots.

A method of and a computer program for analyzing models of dry pipe systems. The computer program includes a user interface and a model generator in communication with the user interface. The method and computer program provide for modeling a referential dry pipe system as a plurality of interconnected nodes, the nodes corresponding to a pipe connector, the interconnection between the nodes corresponding to a pipe device. The plurality of nodes can define a dry portion and a wet portion with the wet portion including a liquid source and the dry portion including a plurality of interconnected pipes to define a volume of gas. The nodes in the dry portion can define a set of nodal devices open to ambient. The method and computer program can provide for computing fluid time based parameters to simulate the liquid flowing from the wet portion to determine at least flow time of the liquid from the wet portion to the open devices. The method and computer program can provide for verification of the liquid flow time in the model within 20% of an actual liquid flow time in the referential dry pipe system.

A method for estimating future cash flows of an investment instrument (or portfolio of investment instruments) is performed by simulating past performance (i.e., cash flows similar instruments based on actual data of past performance, using the simulated past performance to generate a distribution of possible future performance outcomes of the investment instrument, and using the distribution of possible future performance outcomes to make estimates of the expected cash flow from the investment instrument. In one embodiment, cash flow time series of private equity funds (J-curves) are simulated for fully-liquidated vintage years by scaling an aggregate net cash flow time series from a plurality of fully liquidated funds for that vintage year. The time series is scaled by scalar coefficients calculated based on statistics of the four parameters, internal rate of return, money multiple, depth of curve, and speed to depth, of the aggregated vintage fund J-curves.

The invention discloses a passenger flow analysis system. In the system, a camera head transmits shot video data to a video analysis server; the video analysis server transmits the analyzed data to a data center server for being processed; and a reporting system which can generate data reports according to statistical passenger flow volume, transaction amounts and transaction turnover and other data is arranged in the data center server. The invention can reflect rich passenger flow information to support managers to make scientific decisions according to different statistical data and graphs, wherein the passenger flow information includes passenger flow volume, gender character, passenger flow time character, passenger flow speed character, purchase volume and percent conversion, passenger flow volume difference among ordinary days, weekends and holidays, passenger flow volume time distribution, historical passenger flow comparison, historical year-on-year passenger flow compassion and the like.

The invention relates to a Bayesian network model based public transit environment dynamic change forecasting method. The Bayesian network model based public transit environment dynamic change forecasting method comprises the following steps of screening out various factors affecting public transit passenger flow fluctuation or travel time change; abstracting random jamming conditions of exterior environments and passenger flow or travelling time decision variables into nodes of a Bayesian network, determining a station set and the value range of the station set, and performing discretization on the historical information data of the station set and the value range of the station set; analyzing the influence relation between exterior environment jamming input nodes and passenger flow or travelling time decision nodes and establishing a Bayesian network structural diagram for public transit dynamic environment forecasting; determining a conditional probability table between determinant conditions and the decision nodes; computing the posterior probability when certain public transit passenger flow or travelling time occurs, and accordingly, achieving forecasting of public transit environment dynamic change. Combined with public transit incident detection under the environment of an Internet of vehicles, the Bayesian network model based public transit environment dynamic change forecasting method achieves a dynamic passenger flow time and space change forecasting function and provides data support for daily public transit operation and management.

The present invention relates to the technical field of mobile computing, especially to an urban human traffic prediction method and system. The method comprises the steps of: performing regional division of a city according to an urban road network graph to obtain a plurality of preliminary divided regions; performing clustering of human traffic movement features of each preliminary divided region to obtain regions after clustering; taking human flow time features, human flow space features and human flow speed features as feature extraction standards to perform feature extraction of the regions after clustering; and performing fusion of the human flow time feature extraction data, the human flow space feature extraction data and the human flow speed feature extraction data, taking the fused data of the human flow time feature extraction data, the human flow space feature extraction data and the human flow speed feature extraction data as input date input into a graph convolutional neural network structure for training, and obtaining a training result, namely an urban human traffic prediction result. The present invention further discloses an urban human flow prediction system. The urban human traffic prediction method can effectively predict human flow and has high prediction precision.

The equipment for monitoring micro circulation body flow time-space response characteristic on mesentery includes an optical path and an imaging system. In the optial path, the laser bean from helium neon laser is coupled to fiber bundle to form homogeneous diffused beam; and the imaging system consists of stereo microscope with CCD camera, image collecting card with image collecting and controlling software. The CCD signal is output to the image collecting card connected to PC, the collection parameters are set via the image collecting and controlling software and local blood flow information are obtained with the image signal and via the signal analysis software. The equipment provides one new way to the research of influence of medicine and other stimulation to the micro circulation blood flow on mesentery.

Disclosed are hydrophobic finish compositions and cementitious articles made with the hydrophobic finish compositions. In some embodiments, the article is a waterproof gypsum panel surface reinforced with inorganic mineral fibers that face a flexible and hydrophobic cementitious finish possessing beneficial waterproofing properties. These waterproof gypsum panels have many uses, such as, tile backer board in wet or dry areas of buildings, exterior weather barrier panel for use as exterior sheathing, interior wall and ceiling, and roof cover board having water durability and low surface absorption. The flexible and hydrophobic cementitious finish can include fly ash, film-forming polymer, preferably silane compound (e.g., alkyl alkoxysilane), an extended flow time retention agent including either one or more carboxylic acids, salts of carboxylic acids, or mixtures thereof, and other optional additives. Preferably a pre-coated non-woven glass fiber mat is employed to provide the inorganic mineral fibers for the surface reinforcement.

The invention discloses a network flow abnormality analysis method based on visualization clustering. The method comprises the steps: 1) pre-treating monitoring data record of network flow; 2) performing RadViz visualization clustering to the flow data to obtain similar flow time-slot clustering of the network flow features; 3) concluding network flow features of the flow time-slot clustering obtained in the step 2) to obtain abnormal clustering of the network flow features; 4) filtering and choosing the flow time-slot clustering obtained in the step 2) to obtain time-slot points which are discrete in the clustering; and 5) analyzing the time-slot points and the discrete time-slot points in the abnormal flow clustering obtained in the steps 3) and 4) in combination of IPPort matrix. According to the network flow abnormality analysis method based on visualization clustering, the collaborative filtering of the network flow can be performed rapidly in multiple perspectives and the abnormity of the network flow can be analyzed efficiently.

Provided is a micro fluidic device that can control a flow time of micro fluid by using a capillary phenomenon. The micro fluidic device includes: a flow channel for flowing fluid, the flow channel being formed between a top substrate and a bottom substrate or between a top substrate, a bottom substrate, and a middle substrate; a flow blocking surface for stopping a flow of the fluid in the flow channel temporarily; and a hump for delaying the flow formed in the line of continuity with the flow blocking surface. The micro fluidic device of the present research can control the flow time in a simple manner.

The present invention provides improved gas injectors for use with chemical vapour deposition (CVD) systems that thermalize gases prior to injection into a CVD chamber. The provided injectors are configured to increase gas flow times through heated zones and include gas-conducting conduits that lengthen gas residency times in the heated zones. The provided injectors also have outlet ports sized, shaped, and arranged to inject gases in selected flow patterns. The invention also provides CVD systems using the provided thermalizing gas injectors. The present invention has particular application to high volume manufacturing of GaN substrates.

In accordance with the present invention, there are provided toughening agents which are useful for improving the performance properties of epoxy-based adhesive formulations. For example, epoxidized polysulfides have been found to be useful toughening agents of component level underfill adhesive compositions. Invention materials are liquid rubbers which provide improved fracture toughness while maintaining satisfactory capillary flow properties. Invention materials can be synthesized in neat (solventless) reactions from readily available low-cost raw materials and isolated in high yields. They have linear and branched telechelic structures with terminal epoxide functional groups, and are prepared without substantially increasing the molecular weight of the starting polysulfide materials. Invention materials are compatible with common epoxy formulations and may be used without purification. At low levels of incorporation, they provide adhesives that meet the minimum fracture toughness (Gq>2.0 lb/in) and capillary flow specifications (flow time<180 seconds) for many commercial underfill applications. In accordance with a further embodiment of the present invention, there are provided adhesive compositions comprising invention compounds and methods for use thereof. In additional embodiments of the present invention, there are provided methods for the preparation of invention toughening agents, methods for adhesively attaching a device to a substrate, and assemblies comprising first article(s) adhered to second article(s).

The invention discloses a bus passenger flow evolution analysis method based on time-space data fusion. The method comprises the following steps: 1, a historical time-space data preprocessing step; 2, a step of calculating trip get-off probabilities; 3, a bus passenger flow time-space evolution analysis step; and 4, a multi-dimensional passenger flow time-space distribution feature analysis step, i.e., according to an obtained time-space network and flow of corresponding time-space network arc segments, drafting a bus time-space distribution feature graph. The analysis method provided by the invention has the following advantages: 1, the trip get-off probabilities are obtained based on enormous historical time-space data statistics, the precision is quite high, the scheme is simple and practical, the solving efficiency is high, and correlation operation indexes of a whole city bus system can be rapidly solved; and 2, the real-time performance is good. Besides, based on the calculated and stored trip get-off probabilities, station OD is calculated in real time through combination with bus get-on passenger flow, and accordingly, the time-space evolution rule of line passenger flow can be dynamically reflected in real time.