2042 results about "Occurrence probability" patented technology

The present invention is intended to allow a user to easily and precisely extract related terms through use of mutual information without requiring morphological analysis or syntax analysis, by constituting a related term extraction apparatus from preceding-and-subsequent term extraction means for extracting a preceding term occurring prior to a specified term or a subsequent term occurring subsequent to the same in text data; a frequency calculation means for calculating the occurrence frequencies of the specified term, the preceding terms, and the subsequent terms; probability-of-occurrence calculation means for calculating the occurrence probabilities of the preceding and subsequent terms together with the occurrence probability of the specified term; probability-of-concurrence calculation means for calculating the probabilities of the preceding and subsequent terms cooccurring with the specified term; order-dependent degree-of-association calculation means for calculating an order-dependent degrees of the preceding and subsequent terms cooccurring with the specified term; order-independent degree-of-association calculation means for calculating an order-independent degrees of occurrence of the preceding and subsequent terms with the specified term; and term group extraction means for extracting from the text data a group of terms related to the specified term, on the basis of the degree-of-association information calculated by the order-independent degree-of-association calculation means.

The invention belongs to the technical field of network security and particularly relates to an attack-oriented network security situation prediction method, device and system. The method comprises the following steps: detecting and collecting alarm data and network environment operation and maintenance information in a network countermeasure environment, obtaining an element set required by network security situation prediction, wherein the element set comprises three types of information of an attacker, a defense party and a network environment; evaluating the attacker capability and the level of the defense party, establishing a dynamic Bayesian attack graph, and calculating an attack phase number and an attack state occurrence probability vector; and combining a vulnerability scoring standard and network asset information, and performing time-space dimension quantification on the network security situation value. According to the method, dynamic association of the situation elements of the defense party, the attacker, the environment information and the like is achieved, the actual environment of the network is better conformed to, the future situation and the attack occurrencetime can be accurately predicted, higher prediction efficiency is achieved, and storage scale and timeliness of network security situation awareness are optimized, so as to provide more effective guidance for network protection.

A data redundancy elimination system. In particular implementations, a method includes storing in a memory one or more aggregation trees, each aggregation tree comprising one or more base chunk nodes and one or more super chunk nodes, wherein each base chunk node comprises a chunk signature and corresponding raw data, and wherein super chunk nodes correspond to child base chunk nodes and include a chunk signature; receiving a data block; dividing the data block into a plurality of base chunks, each base chunk having a degree value characterizing the occurrence probability of the base chunk; computing chunk signatures for the plurality of base chunks; applying a super chunk rule to contiguous sequences of base chunks of the plurality of base chunks to create one or more aggregation trees, wherein the super chunk rule aggregates base chunks based on the respective occurrence probabilities of the base chunks; identifying one or more nodes in the one or more created aggregation trees that match corresponding nodes of the aggregation trees in the memory; compressing the received data block based on the identified nodes; and conditionally adding the one or more created aggregation trees to the memory.

The invention provides a multi-modality medical image identification method based on deep learning. The method includes the steps that on the basis of multi-modality medical images of a patient's part to be detected, a registration method is adopted to display the multi-modality medical images in the same three-dimensional space; on the basis of the multi-modality medical images, R-CNN is adopted to identify lesion areas in the multi-modality medical images; according to coordinates of the lesion areas in the multi-modality medical images, lesion bodies are displayed in the same three-dimensional space, and according to image blocks corresponding to diagnosed lesion areas, a dense sampling method and a CNN are adopted to obtain occurrence probabilities of various preset disease types. The invention provides a multi-modality medical image identification device based on the deep learning. The device includes a multi-modality medical image display module, a lesion area detection module, a lesion body display module and a preset disease-type occurrence probability module. According to the multi-modality medical image identification method and device, the automatic identification of the lesion areas in the medical images is achieved, and effective reference data is provided for further diagnosis by doctors.

The invention discloses a fire disaster detection system and a method thereof. The fire disaster detection system at least comprises two changeable cameras as a first camera of colorful or near infrared mode and a second camera of colorful or white-black mode, for obtaining the video images of detected region; an image collection module connected with two changeable cameras for converting and filtering the video images collected by the two changeable cameras into digital images, to obtain time sequence images; a fire disaster recognition module connected with the image collection module for analyzing the time sequence images, to obtain flame, smoke and fire disaster occurrence probability; and an alarm module connected with the fire disaster recognition module for comparing the flame, smoke and fire disaster occurrence probability with a preset threshold value, and emitting alarm message according to the comparison result. The invention can effectively obtain the fire disaster video images under various backlight conditions, to realize early and reliable fire disaster alarm.

The invention is suitable for the technical field of computers, and provides a word vector training method and system. The method includes: performing word vector training on each training target word in a training sample document, and meanwhile acquiring a window word of each training target word in a context window in the training sample document; predicting the occurrence probability of each window word by using a Skip-gram model; updating a word vector corresponding to the each window word in a word vector library and an intermediate vector corresponding to each non-leaf node in a code path corresponding to each training target word in a Huffman tree; updating whole document vectors of the training sample document through a preset formula; calculating increasing local input vectors of a CBOW model, and then calculating mixed stitching vectors of the CBOW model; setting the mixed stitching vectors as input of a projection layer of the CBOW model; predicting the occurrence probability of each training target word; and finally updating the word vector of each training target word and the intermediate vector corresponding to each non-leaf node in the Huffman tree. The method and the system improve the accuracy of the word vector of each training target word.

A computer-executable method is executed by a CPU as the following steps of: obtaining an interested range in relation to a target typhoon and obtaining historical typhoons within the interested range from a wind and typhoon database; obtaining shortest distances from the respective historical typhoons to the target typhoon within the interested range; choosing a target ground grid point and obtaining normalized extreme wind speeds of the respective historical typhoons corresponding to the target ground grid point; obtaining extreme wind speeds probable for the target ground grid point by calculation according to the normalized extreme wind speeds and the highest wind speed of the center of the target typhoon; and arranging the extreme wind speeds in descending order, arranging the shortest distances corresponding to the respective extreme wind speeds, and obtaining the occurrence probability of the extreme wind speeds at the target ground grid point according to a formula.

The invention discloses a time sequence classification early warning method for a storage device. The method comprises the steps of collecting storage device parameters in real time; cleaning data; performing ARIMA time sequence analysis; and performing logistic regression analysis and early warning mechanism output. Under the background of a big data environment, time sequence prediction analysisis performed by adopting an ARIMA model according to historical data and hard disk SMART information obtained by statistics; the correlation between a SMART eigenvalue and a fault rate of the storagedevice is analyzed; and an eigenvalue more suitable for a Logistic model is selected out to perform classification prediction. A machine learning method is adopted for predicting the fault rate of the storage device, so that the problems of classification singleness and low early warning intensity in final prediction of the storage device are solved, the defects of hysteresis, low accuracy, pooractual early warning effect and difficult application to the big data environment for a disk early warning mechanism in the prior art are overcome, the occurrence probability of each early warning intensity can be predicted, and an effective solution is provided for real-time operation maintenance and monitoring in a data center environment.

A method and apparatus for providing early fault detection and alert generation in a multi-parameter process is provided. The method comprises defining a multi-dimensional space, each dimension representing a parameter of the process; applying an analysis of the process to the space in order to define regions of high and low probability of occurrence; measuring and mapping parameter values onto the space; and generating an alert whenever a parameter value is mapped into a region of low probability. The method and apparatus are also useful for identifying the cause of faults, adjusting the process to avoid faults and predicting a process-specific maintenance schedule.

The present invention provides a fault diagnosis device for optimizing maintenance measures in a system, in particular a flying object. The diagnosis device has a storage unit for storing diagnosis rules, which each have a fault condition indicating the presence of a fault, and a fault conclusion formed by logical combinations of potential fault causes of the respective fault. Furthermore, a logic unit is provided, which logically combines those stored fault conclusions whose associated fault conditions are met and, on the basis of the combined fault conclusions, determines a logical diagnosis result having at least one fault cause hypothesis. A calculation unit subsequently calculates an occurrence probability for each fault cause hypothesis of the logical diagnosis result determined.

A system provides definitions of network states, and identifies a cause for the anomaly upon detection. A traffic measuring portion (characteristic quantity generating portion) counts the number of packets/time slot classified by traffic type with respect to network traffic, generating a characteristic quantity; a memory portion stores information about the characteristic quantity; a portion calculates correlation coefficients between each pair of characteristic quantities classified by traffic type; a portion generates a histogram from correlation coefficients; a portion for determining the severity of an anomaly based on the histogram; a portion evaluates the similarity of an anomaly of interest to a reference anomaly using the occurrence probabilities of correlation coefficients; and a portion assigns a color to each pixel according to its occurrence probability value, generating an n×n picture. Visualization of network state is achieved using probability distribution vectors derived from correlation coefficients obtained from each characteristic quantities pair.

Disclosed is a behavior detection system for detecting an abnormal behavior, can perform dynamic control based on situation information and a profile of each user to cope with an element threatening security of an internal infrastructure of an enterprise, such as information leakage, in BYOD and smart work environment. The system calculates probabilities of behaviors occurring for respective connection behavior elements, calculates standard deviations of the probabilities based on weighting factors and determines whether or not the calculated behavior occurrence probabilities and behavior standard deviation correspond to a normal behavior, existence of an abnormal connection behavior in a BYOD and smart work environment is detected and an abnormal user is detected by examining whether or not an average traffic volume, an average use time and traffic volume with respect to a use time exceeds respective standard values.

A speech recognition method is provided. The speech recognition method includes the steps of (a) receiving a speech from a user; (b) recognizing the speech to generate a recognition result with a score; and (c) according to the score of the recognition result, performing one of the following steps, (c1) preventing from performing an adaptation for an acoustic model but using a utility rate of the speech to learn a new language and grammar probability model when the score is relatively high, (c2) performing a confirmation by the user when the score is relatively low, further comprising: (c21) when the recognition result is confirmed in the confirmation by the user, performing the adaptation in the acoustic model to increase an occurrence probability of the speech and using the utility rate of the speech to learn the new language and grammar probability model, (c22) when the recognition result is rejected in the confirmation by the user, performing the adaptation in the acoustic model to decrease the occurrence probability of the speech.

The invention provides a SAPMAC method for preparing sapphire single-crystal with size above 300mm. In a 300mm sapphire single-crystal growing furnace, technical processes of charging as well as melting material, seeding, shouldering, isometric growing, pulling out, cooling, annealing and discharging are finished. On the basis of the existing SAPMAC method, the invention is greatly improved, so that the SAPMAC method of the invention has the advantages of larger size of crystal, higher quality, more reasonable size, higher utilization rate of material, lower unit production cost and the like, and can produce sapphire single-crystal with the size above Phi320*260mm, high purity, low defect density and favorable optical performance; in addition, the SAPMAC method of the invention can better satisfy demand of sapphire single-crystal with large size and high quality by national defense and military industry field as well as civil field, and reduces defect occurrence probability by controlling seeding structure; meanwhile, the SAPMAC method of the invention controls crystal shoulder structure, decreases shoulder stress and lowers possibility of crystal cleavage by changing pulling speed and cooling rate.

The invention is suitable for the field of a mobile terminal and provides an unlocking method, an unlocking system and the mobile terminal for a touch screen. The method comprises the following steps: a region of the touch screen is divided into at least two independent regions, and an effective region for triggering the touch screen to be unlocked is respectively set in each region; and judgment is made whether the effective regions for triggering the touch screen to be unlocked in the at least two independent regions touch each other or not, and the screen is unlocked if the effective regions touch each other. The unlocking method and the unlocking system unlock the screen by drawing together the two movable unlocking regions for triggering the touch screen to be unlocked, effectively reduce the occurrence probability of misoperation of the touch screen, and also provide users with the fresh feeling and the maneuverability in the aspect of interface experience.

The invention discloses a system and method for monitoring and early warning of high-threat vehicles, and belongs to the field of smart vehicle safety early warning. The system comprises an information identification module, a data communication module, an information processing and determining module and an early warning prompting module. The existence of high-threat vehicles is determined by real-time monitoring license plate number information, vehicle speed information and vehicle distance information of front and trailing vehicles to notify the vehicle drivers to decelerate and avoid high-threat vehicles, reducing traffic accidents. Vehicle driving habit information, such as regulation violation historical information, accident processing historical information and vehicle model information, can be obtained according to identified license plate number information. Whether vehicles in the surrounding are high-threat vehicles is comprehensively determined in combination with vehicle speed and vehicle distance information. The determination method is simple and easy to realize, thereby advancing the early-warning time, and lowering accident occurrence probability.

An apparatus and method for extracting a predetermined number of consecutive characters from a document image; converting the consecutive characters into a set of symbols using layout information obtained from the consecutive characters; and detecting an orientation of the consecutive characters using occurrence probabilities of the set of symbols.

The application provides a method and device for controlling a driverless vehicle and a storage medium. The method comprises the following steps of judging whether the operating state of a master control module is abnormal or not according to vehicle state information detected by a failure detecting module; when the condition that the operating state of the master control module is abnormal is determined, acquiring a failure scene in which the driverless vehicle is and a failure problem by utilizing an auxiliary control module; and controlling the driving state of the driverless vehicle according to a control strategy determined by the auxiliary control module according to the failure scene and the failure problem. According to the technical solution, the occurrence probability that a safety accident is caused by the fact that the master control module of a driverless system works abnormally is reduced, and the problem that personal safety and equipment and property safety can be possibly threatened during research and development of the driverless vehicle is solved.

A behavior prediction apparatus includes an input unit inputting sole behaviors of a human and simultaneous occurrence probability of behaviors, a behavior discrimination unit specifying a behavior pattern based on correspondence between behaviors corresponding to simultaneous occurrence probability and actual behaviors, an information generation/recording unit generating behavior history information of the specified behavior pattern, and to generate and record information of each of a behavior transition probability, a behavior time and a behavior occurrence probability based on the behavior history information, a behavior prediction unit obtaining a starting behavior from the behavior occurrence probability, select another behavior pattern in order of behavior occurrence probability, and add the behavior time to the selected behavior pattern to output behavior prediction information, and a social value calculation unit obtaining a social value per unit time using the behavior prediction information, social value unit information and the prediction period.

The invention relates to a fingerprint template updating method, which includes the following steps: obtaining and storing M minutiae attributes of a new fingerprint when the new fingerprint is verified; combining the M minutiae attributes of the new fingerprint with the N minutiae attributes of the fingerprint template for storage; carrying out two-two comparison of the combined M+N minutiae attributes, so as to remove the lower-quality minutiae attribute of the two minutiae attributes exceeding the preset threshold value a in similarity after K comparison; and extracting N minutiae attributes with highest occurrence probability and the quality exceeding the preset threshold value b in the M+N-K minutiae attributes, so as to be assembled and formed into a new fingerprint template. By the combination and removing process of the minutiae attributes of the new fingerprint and the original fingerprint template, the multiple minutiae attributes with the quality reaching the standard of the preset quality and the highest occurrence probability are extracted finally, so as to form the new fingerprint template, as a result, the false reject rate of fingerprint verification is reduced, and the accuracy of fingerprint verification is improved.

The invention belongs to the field of lithium ion batteries and relates to a negative pole piece. The negative pole piece comprises a negative current collector and a negative material layer adhered onto the surface of the negative current collector; the negative material layer comprises a first negative material layer and a second negative material layer; the first negative material layer comprises a first material area and a second material area positioned on the edge of the first material area and is adhered onto the surface of the negative current collector; the thickness of the first material area is greater than that of the second material area; the second negative material layer is adhered onto the surface of the second material area and is positioned on the edge of the first negative material layer. The negative pole piece provided by the invention has the benefits that the risk of battery short-circuit caused by potential production of lithium dendrites due to relatively low edge coating weight of a conventional negative sheet is reduced, the occurrence probability of safety accidents of the lithium ion battery is reduced, and the safety performance of the lithium ion battery is improved.

The embodiment of the invention provides a video data detection method and device. The method comprises: receiving the frame data of each image frame of the video having been played, wherein the frame data includes watermarking sequential information; respectively extracting the watermarking sequential information from each frame data, and determining the display time of the image frame corresponding to each frame data; and detecting the continuity of the image frame based on the display time of each image frame, and generating the detection result of the video. According to the embodiment of the invention, the video data detection method and device extract the watermarking sequential information included in the frame data to detect the continuity of the image frame, generate the detection result of the video, namely perform accurate statistics of the occurrence probability of the seizing phenomenon and the seizing time point, and improve the accuracy of detection; and moreover, the problem is avoided that the detection progress is slow cased by the manual detection of the play seizing phenomenon, and the detection efficiency is improved while the test technicians' workload is reduced.

The present invention relates to an apparatus and a method for classifying pixels in each frame of a motion picture as foreground or background. According to the invention, the apparatus has a decision unit and adjustment unit. The decision unit classifies a pixel as a foreground pixel, a background pixel or an undefined pixel based on a first threshold, a second threshold and a distribution value of the pixel representing an occurrence probability of a pixel value of the pixel. The decision unit sorts each pixel into a foreground pixel or a background pixel based on the classification result of the decision unit.

The invention discloses a CT (Computed Tomography) value correcting method for cone-beam CT. The CT value correcting method comprises the following steps: scanning air, a water phantom and a bone tissue body phantom under different scanning conditions to respectively obtain projection data; reestablishing an image; removing noise and artifacts by adopting a polynomial fitting method based on a template image; selecting a plurality of interested regions and calculating a mean value of an attenuation coefficient and a standard deviation of the attenuation coefficient; obtaining the value range of the attenuation coefficient and taking the attenuation coefficient of maximum occurrence probability; fitting the attenuation coefficients of the air, the water phantom and the bone tissue body phantom under the different scanning conditions with corresponding ideal CT values to obtain respective fitting curves; performing CT scanning and image reestablishment on a scanned material; obtaining a CT value image according to a material attenuation coefficient image and a fitting curve and finishing the CT value correction. According to the CT value correcting method disclosed by the invention, image noise and artifact can be effectively reduced, the reestablished image of single material reaches the uniformity to the great degree, the precision of the attenuation coefficient of the material becomes higher and further high accuracy for the fitting of the curve of the CT value and the material attenuation coefficient is realized.

The invention belongs to the technical field of continuous casting, in particular to a secondary cooling method for controlling cross cracks in a corner of a microalloy steel slab. The method comprises the steps of controlling the temperature in the corner of a casting blank by a weak cooling mode after the crystal of the casting blank occurs and before the casting blank is separated from a bending section, so the temperature of the corner of the casting blank is not lower than Ae3 temperature which is the highest temperature at which austenite and ferrite coexist under the balance state; changing the weak cooling mode into an intensified cooling mode before the casting blank enters a straightening section, so the temperature of the corner of the casting blank achieves the Ar3 temperature, namely the temperature at which the austenite begins converting to the ferrite in the cooling process of the casting blank; after the casting blank enters the straightening section, continuously using the intensified cooling mode. The secondary cooling mode control of the continuous slab is optimized, and educts with larger dimension and more dispersed distribution are obtained by the weak cooling mode firstly, so the plasticity of the casting blank is greatly increased; then the transgranular ferrite with large proportion is obtained by the intensified cooling mode, the stress is reduced, the occurrence probability of cross cracks in the corner of the casting blank is greatly reduced, and the method is simple and easy to realize.