137 results about "Channel analysis" patented technology

The invention provides a power station equipment state real-time monitoring and diagnosing method and system based on cloud edge collaboration, and the method comprises the steps: adjusting a data collection period dynamically determined based on an adaptive sampling frequency adjustment algorithm, and collecting a vibration signal, a temperature signal and a current signal through a multi-source heterogeneous sensor array disposed in a power station equipment body; carrying out preprocessing by utilizing the edge computing node, generating a compressed feature vector, and uploading the compressed feature vector to a cloud end through an MQTT protocol; a multi-modal data fusion analysis module is started through a cloud, a three-dimensional evaluation matrix of the equipment health state is constructed in combination with historical operation data and environmental parameters of the equipment, and a calculation task distribution strategy between an edge calculation node and the cloud is adjusted in real time according to an evaluation result of the three-dimensional evaluation matrix. Abnormal mode recognition based on a deep residual network and fault source tracing double-channel analysis based on a physical model are executed, fault types and fault reasons are diagnosed, and the accuracy and timeliness of fault diagnosis are guaranteed.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention relates to the technical field of video analysis, in particular to an AI-based video analysis and management system, which comprises a channel evaluation module, a track extraction module, a cross-source fusion module, a content screening module and a scheduling generation module. In the method, key frame optimization is realized by combining frame activity index evaluation in an object identification process, track continuity breakpoint repair is realized based on a displacement sequence and a direction change trend in a track extraction process, and track matching fusion is realized through region and direction cross mapping in cross-channel analysis. Effective channels are identified based on track keeping features in content screening, and a structured identification directory is constructed in combination with label mapping in a scheduling process, so that target identification and track splicing among different channels have a full-process dynamic completion and consistency verification mechanism, the cross-video-source target identification precision and stability can be effectively improved, and the target identification efficiency is improved. And thus, more efficient content screening and structured recognition can be realized in a multi-source video environment.

The invention provides a side channel analysis method and device based on a feature coding convolutional neural network, equipment and a medium. The method comprises the following steps: acquiring side channel energy trace data of target equipment; a pre-trained first neural network model is utilized to perform feature coding and decoding on the energy trace data to obtain coding features, and the first neural network model comprises a feature head coder and a feature head decoder; inputting the encoding features into a feature head encoder of a second neural network model, performing multi-scale feature fusion through a classification sub-model of the second neural network model, dynamically weighting the encoding features by using an attention mechanism, and extracting target leakage features; calculating guess probability distribution of each candidate key based on the target leakage characteristics; and determining a target key of the target device from the candidate keys based on the guess probability distribution.

The invention discloses a traditional building micro-reconstruction performance improving system based on machine learning. According to the method, the inherent contradiction between traditional building protection and performance improvement is effectively broken through an intelligent multi-dimensional data fusion and generative design technology. A holographic information model constructed by the multi-source data acquisition module completely retains historical genes and space passwords of a building, and provides an accurate space-time reference for subsequent transformation; a two-channel analysis mechanism of the performance evaluation module brings physical structure safety and cultural value continuity into a unified evaluation system, and ensures that a transformation scheme not only meets modern use requirements but also inherits and builds wisdom. The pattern generation and space reconstruction technology of the module is creatively designed, the limitation of traditional craftsman experience inheritance is broken through, contemporary expression of traditional building languages is achieved in the digital twinning environment, and historical style protection is changed from passive limitation to active innovation.

The invention discloses a risk assessment system and method for analyzing abnormal sleep breathing of children based on CBCT dual-channel, and the method comprises the steps: collecting local three-dimensional image data of a maxillofacial region through CBCT, and extracting the sagittal area, volume and multi-dimensional angle and distance parameters of an upper airway through head correction and anatomical mark positioning; and constructing a logistic regression clinical prediction model combining single-factor and multi-factor logistic regression to realize risk prediction. Standardization and equal-interval sampling are synchronously carried out on image data, a multi-channel three-dimensional image data cube is generated, and deep learning classification is realized by inputting the multi-channel three-dimensional image data cube into a 3D ResNet network based on identical fast connection. And finally, performing weighted collaborative analysis on results of the two diagnosis channels, and outputting children obstructive sleep apnea risk classification. Through the parameter driving and image learning dual-channel fusion design, the accuracy and applicability of early screening and risk assessment of children obstructive sleep apnea are improved.

The invention discloses a side channel analysis method and device, equipment, a storage medium and a program product, and the method comprises the steps: obtaining a side channel segment related to polynomial multiplication of a secret key in mask-protected Kyber algorithm side channel data, and inputting the side channel segment into a Hamming weight classification model, the Hamming weight prediction value of the polynomial multiplication operation output result is obtained, and the Hamming weight classification model is obtained by adopting public stage data training of mask-protected Kyber algorithm side channel data; generating a candidate value set of different mask shares related to the same key coefficient according to the Hamming weight predicted value; screening out a target key coefficient from the candidate value set; and under the condition that the target key coefficient is successfully matched with the real key, determining that the side channel vulnerability that the key is stolen exists in the Kyber algorithm protected by the mask.

The invention relates to the technical field of building information model optimization, in particular to a large-volume BIM model optimization system based on intelligent loading, which comprises a model analysis unit, a function identification unit, a simplified execution unit and a model recombination unit, and is characterized in that the model analysis unit reads an IFC file and separates component data; the function identification unit outputs a decoration component (first class) set, a non-bearing structure (second class) set and a bearing component (third class) set through geometric and semantic dual-channel analysis, the simplified execution unit performs edge folding on the first class, performs vertex clustering on the second class and skips simplification on the third class, and the model recombination unit binds metadata and constructs a dynamic octree index. The problems that a traditional simplification strategy is single and core information is lost are solved, the model loading efficiency and application precision are improved, and the method is suitable for complex building scenes.

The invention discloses a mobile phone USIM (Universal Subscriber Identity Module) side channel analysis method based on an outlier. The method comprises the following steps: S1, collecting a power consumption signal generated in the process of executing an AES (Advanced Encryption Standard) algorithm by a USIM card; s2, filtering and aligning the acquired power consumption signal to generate standardized side channel curve data; s3, for each key byte position participating in S box operation in the first round of AES, constructing a candidate key byte value, and calculating an S box output value; s4, recognizing recovered and uncertain key bytes by adopting a correlation energy analysis method; s5, deriving S box output byte positions influenced by the uncertain bytes in the second round; s6, calculating the correlation of the candidate value at the position, and generating a correlation feature vector; s7, inputting a local outlier factor algorithm, and calculating an outlier value; and S8, selecting the key byte with the maximum outlier as a final key byte to form a key byte set. According to the invention, the accuracy and robustness of USIM key recovery are improved in a low signal-to-noise ratio scene.

The invention relates to the technical field of remote monitoring, in particular to a multi-source data acquisition monitoring robot based on the Internet of Things, which comprises a morpheme trigger recognition module, a task compression judgment module, a resource tilt execution module, a difference channel analysis module and a path state injection module. According to the method, dynamic switching of voice interaction states is realized through morpheme response distance and silence zone recognition, priority allocation of task resources is completed in combination with a task delay labeling mechanism, an auxiliary path is released through linkage of node states and communication port fields, resource scheduling flexibility is improved, fluctuation trend and structural offset are fused in abnormal recognition, and abnormal recognition accuracy is improved. And in path control, label field double write-in and state register linkage is adopted, a closed-loop mapping relation between a task path and a control state is constructed, and the accuracy and synchronization consistency of path regulation and control are improved.

Side-channel analysis systems and related methods are disclosed. A side-channel analysis system is configured to generate a synthetic side-channel signal for a code for a software program using a side-channel model. The code includes human-readable instructions of a human-readable computing language. The synthetic side-channel signal includes an estimate of an actual side-channel signal that would be generated by a computer executing the code for the software program. The synthetic side-channel signal is generated based, at least in part, on token values for the human-readable instructions of the code and the training data. The side-channel analysis system is also configured to analyze the synthetic side-channel signal to detect one or more of anomalies or vulnerabilities of the software program. A method includes training the side-channel model.

The invention belongs to the field of Internet of Things anomaly detection, and discloses an edge-cloud collaborative anomaly detection method and system for medical Internet of Things equipment, and the method comprises the steps: S1, obtaining the operation state data of the equipment and the physiological state data of a patient according to a set time length, and generating a state vector; s2, generating an equipment event sequence based on the running state data of the equipment, and encoding the physiological state data of the patient into a physiological abstract containing semantic differences; s3, calculating a process deviation score according to the equipment event sequence and the physiological abstract; s4, calculating a final score according to the physiological abstract and the flow deviation score, and determining an abnormal type label based on the final score; and S5, executing a preset response based on the abnormal type label. According to the multi-device linkage anomaly detection method, through double-channel analysis of device response timeliness and operation process consistency, unsupervised modeling and edge-cloud hierarchical deployment are combined, the problems of device collaboration missing, difficult process anomaly recognition and data scarcity are solved, and the multi-device linkage anomaly detection capability is improved.

A gearbox vibration and noise locating method, device, equipment, and readable storage medium. The gearbox vibration and noise locating method includes: acquiring a vibration signal of the gearbox under test conditions, performing a mixed channel analysis based on the acquired vibration signal to obtain a mixed order spectrum, and performing a synchronous channel analysis based on the input shaft, intermediate shaft, and output shaft as reference axes to obtain the synchronous order spectra of the input shaft, intermediate shaft, and output shaft; determining the characteristic order of the problem based on the mixed order spectrum. If the characteristic order of the problem is inconsistent with the typical order table of the shaft gear, the axis where the faulty component is located is determined based on the synchronous order spectra of the input shaft, intermediate shaft, and output shaft. When an atypical fault exists, by performing a synchronous channel analysis based on the input shaft, intermediate shaft, and output shaft as reference axes, the vibration signal can be further refined to obtain the synchronous order spectrum of each axis. By comparing the synchronous order spectra of each axis, the axis where the faulty component is located can be accurately determined, thereby achieving precise positioning.

The application discloses a computing system configured to measure a step response of a channel in an electronic device, determine a feedback voltage added to the channel by a decision feedback equalizer in response to detecting inter-symbol interference in a received symbol, and predict signal integrity of the channel based at least in part on the step response of the channel and the feedback voltage. The computing system may utilize the predicted signal integrity to determine a probability that a feedback voltage added to the channel by the decision feedback equalizer is wrong, and determine a signal integrity of the channel based at least in part on the step response of the channel, the feedback voltage, and a probability that the feedback voltage added to the channel by the decision feedback equalizer is wrong.

A computerized method comprising: tracking user behavior data of users each using a respective mobile device within a channel; detecting a demand surge in the channel based on channel analysis data for the channel, wherein the channel analysis data for the channel are based channel events for the channel; and generating one or more first incentives for one or more first users of the users based on the demand surge and incentive metrics, wherein the one or more first incentives are configured to change user behavior of the one or more first users. Other embodiments are disclosed herein.

This application is applicable to the field of software testing technology, and particularly relates to a side-channel analysis method based on firmware instrumentation. The method includes: obtaining the execution flow in the firmware image of the target device; determining the instrumentation parameters for monitoring the side-channel signals during the execution of the target device based on the execution flow in the firmware image of the target device, and quickly analyzing and locating the instrumentation parameters; performing side-channel signal monitoring through firmware instrumentation according to the instrumentation parameters, to obtain the side-channel data corresponding to each monitoring point and the current operation state of the target device corresponding to each monitoring point, realizing instrumentation overloading and critical operation monitoring; determining the target side-channel analysis data of the target device according to the side-channel data corresponding to each monitoring point and the current operation state of the target device corresponding to each monitoring point; and finally determining the side-channel analysis result of the target device based on the target side-channel analysis data of the target device, improving the quality of evaluation data, and realizing accurate in-depth monitoring and result analysis.

The invention discloses an optical signal channel structure analysis system based on a new generation of mobile communication networking, which belongs to the field of optical communication data, and comprises the following steps: a data acquisition module receives an OTN (Optical Transport Network) signal and an SDH (Synchronous Digital Hierarchy) signal; the OTN channel analysis module carries out channel structure analysis on the accessed OTN signal and carries out OTN demultiplexing and de-encapsulation mapping processing to obtain OTN channel structure information; the SDH channel analysis module is used for carrying out channel structure analysis on the accessed SDH signal and normalizing the SDH signal into SDH channel structure information; the channel structure issuing configuration module carries out regular conversion on the OTN channel structure information and the SDH channel structure information obtained through analysis, and issues the information to the ZYNQ control chip; the channel structure reporting module reports and pushes the channel structure information of the whole port to the rear-end signal processing module; and the automatic polling module performs an automatic real-time polling mechanism on the accessed OTN signal and SDH signal. The system provided by the invention can effectively realize the characteristic of integrated input and output of channelized equipment.

The application discloses a spiral rod bundle multi-channel experimental device and an experimental method based on additive manufacturing, and relates to the technical field of experimental devices.The spiral rod in the experimental device is processed by using additive manufacturing technology, and the clamping piece structure on the outer surface of the spiral rod can ensure that the thermocouple accurately measures the wall surface temperature.The sub-channel temperature measuring assembly is arranged near the inlet and outlet of the experimental device, the fluid temperature of the typical sub-channel is measured, the sub-channel analysis program is combined, the mixing coefficient of the spiral rod bundle multi-channel can be obtained, the experimental data of different experimental conditions can be obtained by adjusting the flow of the experimental loop and the fluid temperature in the lower chamber, and the Darcy friction resistance coefficient of the spiral rod bundle multi-channel can be obtained by recording the indication of the differential pressure transmitter.The experimental data of the application can provide support for the development of the design program of the spiral type rod bundle nuclear fuel.

The invention relates to a high-security hidden communication system, and belongs to the technical field of information security. According to the system, the mark feature and the structural feature of encrypted data are eliminated through a strong encryption technology, the key feature of the encrypted data is eliminated, the key security is ensured, the ciphertext security is improved, and the risk problem brought by side channel analysis to the data encryption security is solved; a double-layer covert channel is realized through a multimedia steganography technology and a flow simulation technology, the concealment of communication behaviors is greatly enhanced, and the problem of insufficient concealment of a single-layer covert channel is solved; the hidden communication technology with both concealment and anonymity is realized by combining the hidden channel with the anonymous network, and the problems that the anonymous network has obvious traffic characteristics and network boundary nodes are easy to expose are solved.

The application relates to the technical field of wireless communication and vehicle-mounted multimedia interaction, in particular to a vehicle-mounted video backhaul and multi-mode audio interaction system, which comprises the following modules: a service shunting module, which is used for determining the priority label of audio stream data according to an identification result; a channel analysis module, which is used for evaluating the channel capacity and interference level of current time-frequency resources; a power domain allocation module, which is used for dynamically calculating a power allocation coefficient and dividing the total transmission power of a vehicle-mounted communication terminal into a first power level and a second power level; a superposition coding module, which is used for generating a multi-layer superposition signal; and a transmission control module, which is used for directly transmitting the multi-layer superposition signal to a base station side through a radio frequency unit in a scheduling-free transmission mode without sending a scheduling request signaling, so that the competition and conflict of high-definition video backhaul and key voice interaction for limited time-frequency resources in vehicle-mounted wireless communication are effectively solved, and efficient concurrency of heterogeneous services is realized.

The present application relates to a kind of password algorithm artificial intelligence side channel analysis method based on dimension reduction and clustering, belong to information security cryptography field.First, the energy trace obtained is analyzed, to determine the area of the energy trace in which the execution of the decryption process is related to secret information operation.Analyze the energy trace of the key operation part, use the method based on sliding window gradient value to divide each operation, obtain energy trace segment.Combined with the implementation of cryptographic algorithm, the energy trace segment is analyzed, and the operation type is identified.Use principal component analysis on energy trace segment for dimension reduction, take the component corresponding to the expected dimension as the principal component of each operation.Combined with the characteristics of energy trace segment, the cluster number obtained by clustering is estimated.Use clustering algorithm to cluster the energy trace segment after dimension reduction, analyze the clustering result to determine the operation type represented by each cluster, obtain the operation type corresponding to the energy trace segment, finally obtain the secret index sequence, realize the efficient analysis of the side channel of cryptographic algorithm.

The present application relates to pathological analysis technical field, specifically to a kind of pathological section intelligent analysis method and system based on deep learning, including the following steps: based on tissue section image, utilize multi-channel analysis tissue structure and dye distribution, adjust collection parameter and optimize image definition and cell structure performance, combine spatial distribution and gray layering relationship, automatically distinguish mutation area and structure boundary, output demarcation structure positioning mark.The present application is through collection sequence optimization, parameter dynamic adjustment and spatial distribution automatic discrimination, microstructure change, dye distribution and cell arrangement relationship are mapped as structure response index, realizes the efficient linkage of collection parameter and tissue characteristics, supports the synchronous identification of spatial mutation, dye layering and demarcation relationship in complex section, greatly improves the objectivity and coherence of area identification, structure annotation and demarcation positioning, avoids feature omission, enhances the integrity of data processing link and the practical depth of medical image analysis.

The invention provides an automatic control system and method for an AES circuit. The system comprises an upper computer and a lower computer. The upper computer comprises an AES data generation logic module and a serial port communication logic module; the AES data generation logic module generates AES circuit input data; the serial port communication logic module sends the AES circuit input data added with the tag bit to a lower computer; the lower computer comprises an AES circuit, a serial port communication circuit and a data processing circuit; the serial port communication circuit converts input data of the AES circuit into parallel data; the data processing circuit analyzes the input data through the state machine and controls the channel analysis device to collect the power consumption of the lower computer during encryption and decryption; and the AES circuit implements encryption and decryption by using the analyzed data. The AES circuit is automatically called by designing upper / lower computer communication logic, automatic acquisition of the test channel analysis device is realized by controlling a flag bit signal during encryption and decryption, and the time for manual encryption and decryption and manual acquisition is saved.

The present invention relates to the field of water transport engineering data processing, and in particular to a digital monitoring and analysis method and system for water transport engineering. The method comprises the following steps: acquiring multi-source monitoring data of the water transport engineering, performing multi-dimensional feature cross-fusion and channel focusing processing, generating channel operation data, deducing the ship's motion trajectory based on the channel operation data, and calculating the speed to obtain the ship's speed data, calculating the ship's immersion depth and load status, generating ship load data, performing overall simulation of the channel based on the speed and load data, deducing the ship's travel trajectory, generating simulated multi-ship travel data, constructing an analysis engine using the speed and load data, generating a channel analysis twin model, using the twin model to perform status analysis and scheduling processing on the water transport data, and completing intelligent monitoring and analysis of the water transport engineering. The present invention realizes a more comprehensive and efficient digital monitoring and analysis method for water transport engineering.

Systems and methods are disclosed for physical side-channel monitoring of a software program. In one embodiment, a method performed by a server for enabling program- specific side channel analysis (SCA) comprises receiving, from an orchestrator, an instruction to execute a first software program, executing the first software program on at least one processor core of at least one processor at the server, and sending measurement data for the first software program to a SCA monitor. The measurement data for the first software program comprises, for each processor core for each processor on which the first software program is executed at the server, for each time period during which the first software program was executed on the processor core, one or more physical side-channel measurements related to the processor core made during the time period during which the first software program was executed by the processor core.

The invention relates to the technical field of side channel analysis, and particularly provides a side channel analysis method and system based on bit decoupling and a medium, and the method comprises the steps: obtaining power consumption data during encryption, calculating an intermediate value, and carrying out the multi-label decoupling; extracting multi-scale features from the preprocessed energy traces, performing weighted fusion, and outputting global shared features; mapping the global shared features into initial bit features through linear projection, calculating a dependency relationship between the bit features by using an attention mechanism, and generating interactive bit features; the fusion proportion is dynamically adjusted through gating parameters, fusion features are input into a multi-label classifier, the posterior probability of each bit is obtained, and a loss function is adopted for training; and inputting a to-be-measured energy trace into the trained neural network model, outputting a prediction probability, and selecting a target key by calculating the confidence coefficient of the candidate key through traversal. According to the method, the quantity of required energy traces is greatly reduced, and meanwhile, the attack success rate in a low signal-to-noise ratio environment is remarkably improved.

The invention relates to the technical field of network space security, and particularly discloses a security assessment method and device based on side channel analysis, and the method comprises the steps: constructing a ciphertext, collecting L side channel leakage traces of a cryptographic device, calculating the probability distribution of key guessing, and obtaining the mean value and variance of the probability distribution; calculating an optimal variance threshold, and dividing the probability of key guessing into a perfect prompt and an approximate prompt through the variance threshold; integrating the divided perfect prompt and approximate prompt into a DBDD instance through a DDGR framework, converting the DBDD instance into a USVP instance, performing BKZ-beta lattice reduction on the USVP instance, and calculating to obtain a safety parameter beta; and realizing security evaluation based on side channel analysis based on the security parameter beta. According to the method, a perfect prompt threshold optimization method is introduced, and the prompt quality can be dynamically judged according to statistical characteristics, so that the curve quantity required by each secret key is reasonably distributed, and the overall attack efficiency is improved.

The invention belongs to the technical field of network space security, and particularly discloses a side channel analysis method and system for CKKS embedded implementation, and the method comprises the steps: obtaining an electromagnetic trace when CKKS embedded equipment executes a target algorithm; for the obtained electromagnetic trace, constructing a template based on the operation being executed, positioning the leakage position, and identifying the leakage position; inputting the electromagnetic traces for identifying the leakage positions into the trained interconnected convolutional neural network to obtain prediction probability distribution of each category, and then obtaining predicted leakage categories; and constructing a factor graph of a target algorithm, integrating relationships among various leakage information through a belief propagation algorithm, and optimizing a predicted classification result to obtain an optimized leakage category. According to the method, the deep neural network is designed by considering the leakage characteristics of bit stitching, and the relationship between the leakage information is constructed by adopting the factor graph, so that the final side channel analysis result is obtained, and the side channel analysis has higher accuracy.

The invention relates to the technical field of integrated circuit chips, and discloses an integrated circuit chip power consumption information side channel analysis method, which comprises the following steps of: acquiring original power consumption data in chip operation, setting an initial format consisting of a timestamp and an amplitude field, and associating the data with the format through an interface protocol; performing initial processing such as noise filtering and amplitude calibration on the original data, extracting characteristic parameters such as peak amplitude, fluctuation frequency and duration, dividing data types according to a characteristic parameter range corresponding to a chip model, and performing classified storage; establishing a standard mode database, and calling a corresponding standard mode according to operating environment information such as working voltage and environment temperature; comparing the preprocessed data with a standard mode, and marking abnormal data points; and finally, counting an abnormal proportion and transmitting the abnormal proportion to a monitoring terminal, rechecking abnormal data points according to a fixed proportion, and outputting an analysis conclusion according to a rechecking result. The method has good practicability and application value.

The invention provides a side channel analysis method based on frequency domain loss auto-encoder data enhancement, which comprises the following steps of: adding a frequency domain loss item to a reconstruction loss part of a vector quantization variational auto-encoder loss function, and synchronously utilizing frequency domain and time domain information to improve the loss function of an auto-encoder; constructing an auto-encoder model based on the improved loss function, and training the improved auto-encoder model by using original data; performing data enhancement by using the trained model to generate a new energy trace; and finally, performing side channel analysis by using the newly generated energy trace. The method can better help the model to learn the periodic structure and frequency characteristics of the energy trace, thereby generating more real energy trace data, expanding the scale of a data set for side channel analysis, improving the generalization ability of a deep learning side channel analysis model, and improving the effect of side channel analysis.