173 results about "Network error" patented technology

A distributed network-attached storage network provides content distribution using conventional file transfer protocols such as NFS and CIFS. A filer proxy accepts a client request and translates the client request to a file transfer protocol accepted at the file system having the file requested in the client request. The filer proxy generates a file handle for the file containing redundant filer proxy information to be used for failover to a backup filer proxy in the event of a network error or failure of an original filer proxy. The file handle also contains information for network security purposes such as detection of forged file handles.

A system, method, and computer-readable medium for detecting errors on a network. According to a preferred embodiment of the present invention, a network error manager retrieves a network topology from a master subnet manager, wherein the network includes a collection of devices coupled by a first interconnect type. When a connectivity failure is detected in the first interconnect type, the network error manager receives from the master subnet manager at least one event notification via a second interconnect type. An error log analysis component identifies at least one device among the collection of devices as a possible cause of the connectivity failure in the first interconnect type. The network error manager retrieves events from at least one device among the collection of devices that can influence a state of the first interconnect type.

A backup method for a computer system network avoids generating hashes from data that may be inaccurate due to network errors affords verification of source data written to backup media includes reading at a network client a portion of the source data from a source storage volume and generating a hash of the source data at the client. The source data and hash are transmitted to a backup server and written to the backup media. The hash is written to the backup media is associated with the backup data corresponding to the source data from which the hash was generated. Verification of the backup data is performed by reading the backup data from the backup media, generating a hash from the backup data read, and comparing that hash with the hash originally stored on the backup media. This enables the backup data to be verified as corresponding to the original source data without the necessity of rereading the source data from the source volume, and enables faster, more accurate off-line verification subsequent to the backup process.

Embodiments of the invention provide a scalable method for analyzing a network. The method includes generating a data trace representative of the network, determining a topology for the network from the data trace, and sequentially analyzing each frame in the data trace to determine at least one of network errors, warnings, and metrics. The analysis of each frame generally includes analyzing a first protocol layer of the frame with a first expert engine, determining if a second protocol layer is present in the frame, passing the frame to a second expert engine, and passing the frame to additional expert engines if the frame contains additional protocol layers.

A service provider network includes an automated support module that receives stream of network packets from subscribers and separates this streams into packet flows for analysis. The automated support module may be an intermediate device that examines each packet flow to detect subscriber characteristics for each subscriber. The automated support module may apply a set of analysis rules to the detected characteristics to aid diagnosis of reported or potential network errors.

The invention discloses a method for automatically matching multisource space-borne SAR (Synthetic Aperture Radar) images based on an RFM (Rational function model). The method comprises the following steps of: calculating respective RPC (Rational Polynominal Coefficient) parameter of images; performing forecast of initial positions of points to be matched, matching of approximate epipolar line geometric establishment constraints and geometric rough correction of matched window images by using the RPC parameters of the images on every pyramid image layer, deleting wrong matching points from the image matching result of every layer of pyramid by adopting regional computer network error compensation based on an RFM model; refining the RPC parameters of the images and calculating the object space coordinates of the matching points; refining the matching result to the original image layer by layer; and refining a matching result by using a least square image matching method to realize automatic and reliable matching of common points of multisource space-borne SAR images. In the method, the RFM model is introduced into automatic matching of the multisource space-borne SAR images, and the regional computer network error compensation of the RFM model is blended into the image matching process of every layer pyramid, so that wrong matching points in the matching process can be effectively deleted, and the workload of manual measurement of common points is effectively lowered.

The invention relates to an inertia/visual integrated navigation method adopting iterated extended Kalman filter and a neural network, belonging to the technical field of integrated navigation in a complicated environment. The method comprises the steps of when a visible signal is valid, acquiring a dynamic video by utilizing a camera carried by a mobile robot, and determining the speed of the camera by an image characteristic extraction method and a nearest neighbor matching method; optimally estimating the speed and the acceleration of the mobile robot by using the iterated extended Kalman filter; establishing a navigation speed error model of an inertial navigation system by utilizing the neural network; when the visible signal is in loss of lock, compensating the speed error of the navigation system by virtue of the neural network error model which is previously obtained by training. According to the method, the problem that the inertia/visual integrated navigation system can not provide lasting high-precision navigation when the visible signal is in loss of lock can be solved; the method can be applied to long-endurance, long-distance and high-accuracy navigation and location for the mobile robot in the complicated environment with weak light, no light or the like.

Method, apparatus and computer program product for fault recovery in a distributed stateful publish-subscribe system. The system includes the capability of recovering from failures that may occur when a stateful publish-subscribe service is implemented on an overlay network. Such failures may include, for example, temporary crashes of broker machines, and network errors causing messages to possibly be lost, duplicated or delivered out of order. The system requires stable storage logging only when a published event enters the system, and requires that logged messages be retrieved from stable storage only in the event all brokers between a failed link or broker and the publishing sites have failed. The publish-subscribe system of the present invention does not require that broker-to-broker connections use reliable FIFO protocols, such as TCP/IP, but may advantageously use faster, less reliable protocols.

An apparatus and method for a database query execution monitor determines if an network error or low performance condition exists and then where possible modifies the query. The query execution monitor then determines an alternate query execution plan to continue execution of the query. The query optimizer can re-optimize the query to use a different network or node. Thus, the query execution monitor allows autonomic error recovery for network failures using an alternate query execution. The alternate query execution could also be determined at the initial optimization time and then this alternate plan used to execute a query in the case of a particular network failure.

The invention discloses a flood forecasting method based on cluster analysis and real time correction, which comprises the following steps: 1) using PCA(Principal Component Analysis) to perform dimensionality reduction to the input of a model; 2) using the K-means clustering method to conduct clustering analysis on original data; dividing the flood data into different classifications; and then training different SVM models; when a testing sample is inputted, using the clustering center to determine the classification of the test sample and predicting the corresponding model to obtain a predicted value q; and 3) using a BP neural network for real time correction; calculating the error sequence between the predicated value and the actual value; using the error sequence data to train the BP neural network error correction model to obtain the error correction value qe. The final forecasting result is the model predicted value q plus the error correction value qe. According to the invention, the original hydrological data are divided into several classifications by cluster analysis, and through the training of the models, forecasting can be available by the multiple models. Then, real-time correction is achieved by the BP neural network to improve the forecasting accuracy for the time of flood peak.

The invention provides a combined pre-coding and power allocation method of the multi-cast network based on the network coding in the wireless communication field. In the invention, a multi-cast network formed by two-source, two-place and arbitrary relaying cluster is adopted. Two sinks need to obtain the information of each source and can not cross-transmit the signal due to the limit of the sending power, so the relaying node is responsible for forwarding information of each source. Through introducing simulation network coding into the relaying, namely, the received aliasing signal of the two sources is broadcast out after linear amplification, thus minimizing the transmission time to the maximum extent. The invention improves the network error frame performance and enhances the overall through put of the network by introducing the combined pre-coding and power allocation.

The invention discloses a lake and reservoir algal bloom predicating method based on multielement nonstationary time series analysis and neural network and support vector machine compensation, and belongs to the technical field of water quality monitoring. The method comprises the steps of characteristic factor nonstationary time series modeling, error influence factor kernel principal component analysis, neural network error modeling according to the situation of large sample data, support vector machine error modeling according to the situation of small sample data, final error compensation and predicating result obtaining. The problems that existing algal bloom predication precision is not high, and predication is hard to carry out according to the small sample data are solved, the description of the algal bloom forming process corresponds to reality better, and the result of algal bloom modeling predication is more accurate. The advantage compensation of a time series analysis method suitable for linear system modeling and a statistical learning method suitable for nonlinear system modeling is achieved, and the algal bloom predication accuracy is improved.

A videophone system providing high resolution video transmission between videophones utilizes compressed video signals and audio signals which may be transmitted through any communications network, with the system providing real time adaptive error recovery and synchronization between the audio and video signals to produce high quality video images and sound. The system is highly resilient, adapting in real time to changing conditions in a network or to network errors due to data corruption or loss that can be produced, for example, by noise or line losses, thereby substantially eliminating fitter, signal packet loss or delay, or other errors which produce signal degradation.

A system and method for efficiently and accurately detecting errors in a network is provided. Devices coupled to a network are configured to determine their local network status and transmit their local network status to a centralized location. The centralized location is configured to receive the local network status from each device and provide network status for the entire network. The centralized location is also configured to analyze the collective network status information and report any network errors. The centralized location is further configured to present the information in a format that allows a user of the network to quickly determine the configuration and status of the network (such as a computer generated graphical representation of the network).

The present disclosure proposes a network diversity based error reporting method and a user equipment using the same method. The user equipment using the proposed method in the event of a network error would establish a first connection with a first network, establish a second connection with a second network which is a different type of network from the first network, establish a first failure report corresponding to the first connection in response to failing to connect with the first network, transmit through the second connection the first failure report to the second network in response to establishing the first failure report, establish a second failure report corresponding to the second connection in response to failing to connect with the second network, and transmit through the first connection the second failure report to the first network in response to establishing the second failure report.

The invention provides a method for training an acoustic model based on CTC (Connectionist Temporal Classification). The method comprises the steps of 1, training an initial GMM (Gaussian Mixture Model), wherein time point forced alignment on text annotation of training data by using the GMM to obtain a time region corresponding to each phoneme; 2, inserting a blank symbol associated with the phoneme behind each phoneme, wherein each phoneme has a unique blank symbol; 3, constructing a CTC forward and backward calculated search path diagram for a phoneme annotation sequence with the blank symbols being added by adopting a finite state machine; 4, restricting the appearance time range of each phoneme according to a time alignment result, pruning the search path diagram, and cutting off thepath with the phoneme position exceeding the time restrictions so as to obtain a final search path diagram required by calculating a network error in CTC; and 5, performing acoustic model training byadopting the combination of a time-delay neural network (TDNN) structure and the CTC method to obtain a final TDNN-CTC acoustic model.

A method, system, and program for managing network errors communicated in a message transaction with error information using a troubleshooting agent. A network facilitates message transactions between a requester and a responder for facilitating web services. When a non-application specific error occurs in relation to a particular message transaction, such as a network error, a protocol layer assigns an error code and either the requester or responder encodes the error code in the body of an envelope added to the particular message transaction. In particular, the message transaction is an XML message with a Simple Object Access Protocol (SOAP) envelope encoded with the error code to which the XML message is then attached. The error encoded message transaction is forwarded to a troubleshooting agent. The troubleshooting agent facilitates resolution of the non-application specific error and returns a descriptive message indicating the resolution of the non-application specific error to at least one of the requester and the responder.

The invention relates to a three-dimensional image matching method based on a convolutional neural network, which comprises the following steps: acquiring a plurality of three-dimensional matching image pairs and corresponding real disparity, and taking the three-dimensional matching image pairs and the corresponding real disparity as a data set; Constructing a convolutional neural network, and selecting a linear correction unit (RELU) function for activation; Training the convolutional neural network by adopting a back propagation algorithm, and determining a network error function and a learning rate; Through the calculation of the convolutional neural network, the network outputs a matching cost space diagram of the left and right image blocks; And performing matching cost aggregation,disparity selection and disparity refinement on the cost space graph, and selecting a pixel point with the minimum cost as a matching point to obtain a final disparity map. The image can be directly used as network input, the overall disparity map obtained through the convolutional neural network matching algorithm is relatively smooth, a relatively good matching effect can be achieved in a non-texture region and a depth value abrupt change region, and relatively good robustness is still achieved for an image pair with illumination change and incomplete correction.

The invention provides a thermosensitive thermometer calibration method based on a neural network. The method comprises the following steps: 1) obtaining sample data value, comprising measured temperature in a certain section and a corresponding thermistor value; 2) carrying out neural network model arrangement; 3) training a thermosensitive thermometer calibration compensation model; and 4) correcting the measured result of a thermosensitive thermometer by utilizing the obtained calibration compensation model, wherein the step 3) also comprises the following steps: step31) initialization of network parameters, step 32) initialization of the neural network, step 33) calculation of network errors and step 34) network training. The method fully considers nonlinear characteristics of measurement of the thermosensitive thermometer, constructs the calibration compensation model by utilizing the feature that the neural network can approximate to a nonlinear function better, and can improve measurement accuracy of the thermosensitive thermometer; and besides, the method does not need to additionally increase a peripheral hardware circuit, does not increase test cost, and has a certain practicability.

The invention relates to a spacecraft attitude stability judgement method utilizing RCS. The method is characterized in that the steps are as follows: when the format of a RCS sequence is correct, the RCS sequence is segmented, discrete wavelet transform is then carried out, so that a discrete wavelet transform coefficient is obtained, and wavelet transform characteristics are extracted; the transform characteristics are normalized and then inputted into a three-layer BP (back-propagation) neural network, and when a BP neural network error function E (W) reaches a minimum value, the attitude stability of a spacecraft is judged according to the output value of the BP neural network; 0 in the output value represents attitude stabilization, and 1 in the output value represents attitude rolling. The method can utilize the characteristics to judge the attitude stability of the spacecraft, and measured data prove that the space target attitude stability identification rate of the method is more than 90 percent and that the operation speed is high ( single target identification time is less than 2s).

A method for processing video stream data, including: acquiring video source information of video streams having multiple resolutions; selecting, according to the video source information, a video stream having a highest resolution as a target video stream, and playing the target video stream; switching to a lower resolution video stream, if a network error or a decoding error occurs in the playing process; and playing the lower resolution video stream. Is addition, embodiments of the present invention further provide a corresponding apparatus and system for processing data of a video stream.

An apparatus for receiving a voice signal and data, and a controlling method thereof, by which a voice telephone call can be made even in power failure, network error, or CPU error, comprises: a splitter for dividing data transmitted from a data transmission unit into an internet protocol data packet and a voice signal; a first telephone connected to an analog POTS channel of the splitter for receiving the voice signal divided in the splitter; and an integrated access device (IAD) for connecting a second telephone to a voice line, that is, the analog POTS channel of the splitter or connecting the second telephone to a data line of the splitter according to state of power failure.