116 results about "Long-term memory" patented technology

A hierarchical folder security system for mapping files into and out of alias directories and / or real directories depending on: 1) the directory of the specific file being accessed, 2) the program thread requesting access to a memory storage device (114), and 3) the type of access request being made (i.e. load, save, run, etc.). For write access requests (i.e. save, modify, paste, delete, cut, move, rename, etc.), security controller (180) determines if the requested folder access address (150a) is associated with the requesting program's folder address (168). If it is, the file is written at absolute address (156b) on disk platter (160) through real folder map (152). If requested address (150a) is not associated with program folder address (168), then an alias directory address is created and the file is written at alias address (156a) through alias folder address map (154).

A minimally invasive, implantable heart sound and ECG monitor and associated method for deriving blood pressure from heart sound data. The device is equipped with an acoustical sensor for detecting first and second heart sounds which are sampled and stored during sensing windows following R-wave and T-wave detections, respectively. ECG and heart sound data are stored in a continuous, looping memory, and segments of data are stored in long-term memory upon an automatic or manual data storage triggering event. Estimated blood pressure is calculated based on custom spectral analysis and processing of the first and second heart sounds. A calibration method includes measuring a patient's blood pressure using a standard clinical method and performing regression analysis on multiple spectral variables to identify a set of best fit weighted equations for predicting blood pressure. Concurrent ECG and estimated blood pressure may be displayed for review by a physician.

A blocking device provides read and write protection for computer long-term storage devices, such as hard drives. The blocking device is placed between a host computer and the storage device. The blocking device intercepts communications between the host and the storage device and examines any commands from the host to the storage device. Certain commands, such as commands that may modify the storage device, may be discarded.

The system of the invention includes a method for monitoring changes in the status or condition of a Container using one or more monitoring units mounted to the Container. The monitoring units preferably include a power supply, sensors using reflective energy with programmable parameters, globally-unique sensor identification, recording capability on a timeline, long term memory and the ability to rebroadcast information on RFID radio technology. Programmable monitoring hardware in the monitoring unit detects significant changes in the sensor outputs as a triggering event. The programmable monitoring hardware includes memory for storing identification information for the Container. The sensors which can include conventional devices that detect various forms of energy including visible light, infrared light, magnetic fields, radio frequency energy and sound. In one embodiment, a monitoring unit is mounted inside a shipping Container suitable for long distance transport. The triggering event can be used for tamper detection security.

The invention relates to methods and products for enhancing and improving recovery of lost memories. In particular the methods are accomplished through the increase of histone acetylation.

A blocking device provides read and write protection for computer long-term storage devices, such as hard drives. The blocking device is placed between a host computer and the storage device. The blocking device intercepts communications between the host and the storage device and examines any commands from the host to the storage device. Certain commands, such as commands that may modify the storage device, may be discarded.

A system and method for storing and processing physiological data in a medical recording device that allows continuous data collection and storage of such data in multiple time-resolved levels are provided. The method includes: sampling one or more physiological signals at a selected sampling rate; deriving physiological parameter values from the sampled signal; storing the parameter values as they are determined in a temporary memory buffer for a predetermined storage interval; determining a statistical aspect of the stored parameter values upon expiration of the storage interval; and writing the statistical aspect to a long-term memory buffer. A number of long-term memory buffers may be designated for storing a statistical aspect of a physiological parameter at uniquely different time resolutions. The resolution of each long-term memory buffer is determined by the storage interval defined for an associated temporary memory buffer, which stores parameter values from which statistical aspects are computed.

A computerized method for collecting suspected data of interest from a computer comprises searching the computer's shot-term memory to locate at least one target memory range containing the suspected data of interest, and copying the suspected data of interest within the target memory range to an alternate data storage location in a manner which avoids writing the suspected data to the computer's long-term memory. Alternatively, the suspected data of interest can be copied to a previously unused data storage location while preserving integrity of non-volatile memory resources. A computer-readable medium and a system for collecting target forensics data are also provided.

The invention provides a medical field-oriented named entity identifying method based on deep learning and relates to a named entity identifying method. The invention mainly aims at solving the problems that semantic information is not considered in a CRF model and a large amount of unmeaning labelling results can appear in labelling results under the condition that training corpus is extremely scarce. The method provided by the invention is realized by the following steps of: firstly, obtaining a vocabulary voc supplementing medical field corpus and a term vector vec corresponding to the vocabulary voc; secondly, carrying out short and long term memory (LSTM) unit network training by utilizing training corpus in labelled corpus; and thirdly, carrying out path finding of labelling results according to an updated neural network parameter theta in the step two, so that the labelling results with labelled corpus; and evaluating labelling result of test corpus in the labelled corpus by utilizing a named entity identification and evaluation criteria value F. The method provided by the invention is applied to the named entity identification field.

The invention discloses an ultra-short-term wind power prediction method based on a small wavelength short-term memory network, which generates input variables according to historical data and takes the corresponding wind power historical data as output to obtain a training sample. Using wavelet analysis method to decompose the training samples into four wavelet samples, and using short-term and long-term memory network model to train the four wavelet samples respectively, the small-wavelength short-term memory network prediction model after training is obtained. According to the actual data of the four wavelet samples at the time to be predicted, the test input data are generated and input to the prediction model, and the output is the ultra-short-term wind power prediction value at the time to be predicted. The invention combines wavelet analysis method with long-term and short-term memory depth network, can realize data stabilization processing and depth learning at the same time, improves prediction accuracy and enhances model generalization ability.

The invention belongs to the technical field of computer vision and natural language processing, and discloses an image-text mutual retrieval method based on complementary semantic alignment and symmetric retrieval, comprising: using convolution neural network to extract the depth visual features of images; Using the model of object-based convolutional neural network and scene-based convolutionalneural network to extract depth visual features to ensure that the visual features contain multiple complementary semantic information of the object and the scene; encoding the text by using short-term and long-term memory network, and extracting the corresponding semantic features. mapping visual features and text features into the same cross-modal embedding space by using two mapping matrices; Using the k-nearest neighbor method, retrieving the initial list in the cross-modal embedding space. Using the neighborhood relation of symmetrical bi-directional retrieval based on mutual nearest neighbor method, the initial retrieval list is reordered and the final retrieval level list is obtained. The invention has the advantages of high accuracy.

A mass flow controller and method for operating the same is disclosed. The mass flow controller includes a mass flow control system to control the mass flow rate of a fluid, and a data logging component that obtains snapshots of condition-specific-data for each of a plurality of reoccurring condition types, and reduces each snapshot of condition-specific-data to functional parameter values that characterize each snapshot of condition-specific-data, and the data logging component generates statistical values that are stored in a short term data store that characterize multiple functional parameter values that are obtained during each separate occurrence of a specific condition type. A diagnostics component diagnoses failures using current functional parameter values and the statistical values stored in the short-term memory, and a prognostics component predicts failures based upon a collection of data sets that are stored in the long-term memory.

The invention discloses a target tracking method based on target template updating. A target template base is established based on particle filtering principles under characteristic fusing and employing a video block multi-task means. The information of the next frame and target information in the target template base are matched and sorted by using color and edge characteristics. Template memory criteria are used to update the target template base, thereby realizing target long-term memory under different states. The target tracking method overcomes the defect that conventional video target tracking technology is likely to be influenced by external interference. Targets can be accurately positioned, and tracking errors can be effectively reduced.

Techniques are described for detecting anomalous events using a long-term memory in a video analysis system. The long-term memory may be used to store and retrieve information learned while a video analysis system observes a stream of video frames depicting a given scene. Further, the long-term memory may be configured to detect the occurrence of anomalous events, relative to observations of other events that have occurred in the scene over time. A distance measure may used to determine a distance between an active percept (encoding an observed event depicted in the stream of video frames) and a retrieved percept (encoding a memory of previously observed events in the long-term memory). If the distance exceeds a specified threshold, the long-term memory may publish the occurrence of an anomalous event for review by users of the system.

A long-term memory network method and system for text comprehension. A recurrent neural network can be provided, which includes an external memory module and a long-short term memory unit, wherein said recurrent neural network encodes raw text information into vector representations, forms memories, finds relevant sentences to answer questions, and generates multi-word answers to said questions utilizing the long short term memory unit.

A computer system and method according to the present invention can receive multi-modal inputs such as natural language, gesture, text, sketch and other inputs in order to manipulate graphical objects in a virtual world. The components of an agent as provided in accordance with the present invention can include one or more sensors, actuators, and cognition elements, such as interpreters, executive function elements, working memory, long term memory and reasoners for object placement approach. In one embodiment, the present invention can transform a user input into an object placement output. Further, the present invention provides, in part, an object placement algorithm, along with the command structure, vocabulary, and the dialog that an agent is designed to support in accordance with various embodiments of the present invention.

Systems and methods for backup of data in redundant data storage systems. In this regard, one embodiment can be broadly summarized by a representative system that copies a block of data from a primary storage unit to a primary backup storage unit using a primary addressing sequence that begins with a first start address; and substantially concurrently copies a second block of data from a secondary storage unit to a secondary backup storage unit using a secondary addressing sequence that begins with a second start address.Another embodiment can be described as a method wherein the first start address is the same as the second finish address; the primary addressing sequence uses an incrementing count, and the secondary addressing sequence uses a decrementing count. Other systems and methods are also provided.

A computer system and method according to the present invention can receive multi-modal inputs such as natural language, gesture, text, sketch and other inputs in order to simplify and improve locative question answering in virtual worlds, among other tasks. The components of an agent as provided in accordance with one embodiment of the present invention can include one or more sensors, actuators, and cognition elements, such as interpreters, executive function elements, working memory, long term memory and reasoners for responses to locative queries, for example. Further, the present invention provides, in part, a locative question answering algorithm, along with the command structure, vocabulary, and the dialog that an agent is designed to support in accordance with various embodiments of the present invention.

The invention belongs to the technical field of road design, which relates to a method for determining lengths of upstream transition regions of a construction operation area. The method comprises the steps of: firstly, designing a driver SOAR (state, operator and result) intelligent agent working memory structure, secondly, designing initial long-term memory rules, and establishing a long-term memory rule base of driver SOAR intelligent agent, thirdly, establishing the decision-making period of the driver SOAR intelligent agent, fourthly, establishing a method for evaluating the road network traffic capacity through road network balance degree, and fifthly, setting lengths of different upstream transition regions and different road load coefficients to perform simulation, to obtain the road network balance degree data under lengths of different upstream transition regions of the construction operation area through simulation, and selecting simulation conditions with high balance degrees and good astringency, to obtain lengths of the appropriate upstream transition regions of the construction operation area under different traffic conditions. The method can accurately reflect the influence on the road construction traffic capacity by the lengths of the upstream transition regions of the construction operation area, thereby determining the lengths of the upstream transition regions of the construction operation area.

Methods are disclosed for instructing a player in a video game in a more effective manner. In an embodiment, a first fact is provided to a player and then the player is asked questions about the first fact, solidly placing the first fact in the player's short-term memory. Then a player is provided a second fact, interrupting the short-term memory of the first fact. The player is then asked a question about the first fact, interrupting the short-term memory of the second fact. A question is then asked about the second fact, interrupting the player's short-term memory of the first fact. Additional facts may be similarly handled. By the use of the strategic interruptions, the facts are pushed into the player's long-term memory deeper and more quickly than with the use of pure repetition of facts while they are in the player's short-term memory. The questions may be incorporated into virtual entities that the player is required to interact with. To improve gameplay, the player's input may be automatically processed once the player enters the expected number of digits associated with the expected answer.

The present invention provides a vision object tracking method based on hierarchical convolution. The method mainly comprises the content consisting of hierarchical convolution, correlation filters, translation estimation from rough to fine, region proposal and model updating. The method comprises the processes of: employing hierarchical features in a convolutional layer, and employing bilinear interpolation to regulate each feature map to a larger fixed dimension; performing normalization of the cycle version of input features to a soft target score generated by a Gaussian function; searchingthe maximum value of a target object on a response map; giving a related response map set; performing hierarchical deduction of each layer of target translation; calculating one confidence coefficient score of each proposal; keeping long-term memory of the target appearance; and finally, performing minimization of output errors to update an optimal filter. The vision object tracking method basedon hierarchical convolution mitigates sampling fuzziness, reduces tracking drift, reduces errors caused by reasons such as illumination change, shielding, background hybridization, sudden movement andtarget drift out of a visual field, and improves identification accuracy and robustness.

The invention discloses a device life prediction method based on multiple long / short-term memory network and experience bayesian, which belongs to the technical field of mechanical equipment state monitoring and life prediction. The method comprises the following steps: 1) a plurality of monitoring signals of the mechanical equipment are obtained, and the collected monitoring signals are preprocessed; 2) the monitoring signals are screened, and signals which can well reflect the degradation process of the mechanical equipment can be selected; 3) a plurality of long-term memory network models are constructed to realize multiple network synchronous training; 4) the signals collected by the sensors in real time are input into the multiple-length short-term memory network model, a plurality ofnetworks are subjected to parallel prediction, and a prediction result is obtained; 5) the probability distribution of the prediction result is estimated through an empirical bayesian algorithm, andthe most probable service life of the equipment is deduced. According to the invention, the remaining service life of the mechanical equipment can be accurately predicted in real time, so that the faults of the mechanical equipment can be sensed in advance, the safety, stability and long-period running of the mechanical equipment can be guaranteed.

The invention includes a teaching machine that presents questions to the user who responds with answers. Graded interval recall is accomplished by storing the exact time and date when the user learns a correct answer. The machine determines the presentation mode and review schedule for each unit according to the length of real time that has elapsed since the user learns the material. Precise timing is designed to bridge the threshold between short-term and long-term memory by interrupting default sequencing of material to review items at an exact preset interval from the time the user learned the material.

A testing device to test how computer systems interact with computer long-term storage devices, such as hard drives. The testing device is placed between a host computer and the storage device. The testing device intercepts communications between the host and the storage device and examines any commands from the host to the storage device. The testing device may respond to an Information ID request from the host with predetermined data, not the storage device's actual data. The testing device may respond to a read or write command to a specific sector with an error message from a predetermined list of sectors and errors.

The present invention is an exercise routine (ER) display system and method that can substitute as a trainer / therapist for the purpose of leading one or more users through a single exercise task or through an entire ER. The device displays a predetermined series of exercise tasks seriatim in a predetermined sequence, displaying each repetition of each task in the proper cadence according to a set of variable parameters that specify how each maneuver is to be displayed. Audible elements of the display count the repetitions or give cues for proper technique. Various other types of information are shown visually. The exercise maneuvers are displayed on a local display module that includes, in one embodiment, data processing components, data input / output components, display components, and sufficient long-term memory capacity to accommodate a large library of exercise maneuvers. In a second preferred embodiment, the memory capacity of the local display module is minimal, and ER data are provided to the module in an ad hoc fashion through a data input means such as CD-ROM, DVD, flash memory, or through a USB, wireless, or Internet connection to a master computer.

A knowledge management system to optimize the human learning and memorization process. Memorization of desired information is achieved through knowledge transfer and rehearsal, whereby a learning system provides a series of associated elements to the user using a dynamical and reactive algorithm that responds to the user's abilities to prepare an efficient rehearsal process. The instant system combines a dynamic learning system with a database that comprises content provided by multiple users and which is overseen by individuals to ensure the validity of the content within the system. Information may be provided in a multimedia format which can include music, video, text and other auditory and visual stimuli as part of the rehearsal process.

An operating system configured to support cognitive capable environments is described. The system comprises a memory structure and an I / O process configured to update inputs and outputs. The operating system further includes a process to determine changes to symbols within the symbol space due to the stimulus and create STM images of the inputs. Additional processes are configured to pass stimulations between connecting symbols, measure temporal and spatial properties of images, filter the STM images based upon the properties, and propagate stimuli through hereditary structures. Further processes analyze propagated symbol groups of STM images for novel distinctions, create new symbols, connect novel stimuli together for symbol groupings, update existing symbols to include connections to the new symbols, and assign weights to the connections. Additional processes form stimulus-response pairs from received images, provide time-based erosion of connection weights of symbols and adapt learned responses to long term memories.

Disclosed is a method for changing emotion of a software robot (sobot) in a sobot terminal. The sobot terminal includes a short-term memory unit and a long-term memory unit. When an emotional change occurs during the operation of the short-term memory unit, the sobot terminal stores a short-term reference emotion value of the corresponding emotion in the long-term reference memory. When the operation time of the long-term memory unit elapses, the sobot terminal resets the long-term reference emotion value using the short-term reference emotion values stored in the long-term memory unit. The sobot terminal can change the emotion of the sobot more naturally through the above process.

The invention provides a method for preparing a 5,6,7,4'-tetrahydroxyflavone compound, which is characterized by comprising the following steps: taking breviscapinun as a raw material, and performing heating reflux reaction between the breviscapinun and alcohol under the catalysis of acid, namely adding 200 to 500 ml of alcohol substance and 1 to 3 ml of 4 to 5 percent acid into 12 to 20 g of the breviscapinun for the heating reflux reaction; and filtering a product, and drying to a constant weight to obtain 5,6,7,4'-tetrahydroxyflavone shown in a formula I. Experiments prove that the 5,6,7,4'-tetrahydroxyflavone shows obvious strengthening effect on acquisition, consolidation and representation of brain memory of a mice model of alzheimer disease, has obvious improving effect on learning memory of the mice model with memory loss caused by anisodine of an M-cholinergic inhibitor, and can obviously improve the recent memory and long-term memory capacities of mice. The experiments also prove that the 5,6,7,4'-tetrahydroxyflavone has the effect of treating arthritis, and also has good treating effects on diabetes mellitus and osteoporosis.