159 results about "Sequence design" patented technology

The invention relates to the technical field of biological information, and provides a thermodynamic parameter-based dsRNA / siRNA target sequence design method, which comprises the following steps of: standardizing coding sequences of candidate genes; presetting a reading frame, and segmenting the coding sequence of the candidate gene through translation of the reading frame to obtain a plurality of candidate dsRNA / siRNA target sequences; the candidate dsRNA / siRNA target sequences are read and analyzed, and base pairs of all the candidate dsRNA / siRNA target sequences are obtained; presetting thermodynamic parameters of all the base pairs, calculating the sum of the thermodynamic parameters of all the base pairs, and obtaining final thermodynamic parameters of the candidate target sequence in combination with the correction parameters; and calculating the score of each candidate dsRNA / siRNA target sequence according to the thermodynamic parameters, and determining the candidate dsRNA / siRNA target sequence with the highest score as the optimal dsRNA / siRNA target sequence.

The invention discloses an engine turbine shaft sequential optimization design method based on ensemble learning, and the method comprises the steps: carrying out the parametric modeling of an aero-engine turbine shaft through employing a finite element method, and determining a design variable; determining an optimized objective function and constraint conditions based on the turbine shaft structure; constructing a training point set by using the target function and the constraint condition, and establishing an ensemble learning agent model by using the training point set through an ensemble learning algorithm; executing a sequential optimization design process based on the ensemble learning agent model; searching a current optimal sequence design point by utilizing an intelligent algorithm and a sequential updating criterion, and judging whether the sequence design point meets a threshold value requirement or not; if the current optimal sequence design point meets the threshold requirement, outputting the current optimal sequence design point as a design result; otherwise, adding the sequence design points as new training points into the training point set to update the training point set, updating the ensemble learning agent model by using the updated training point set, and iteratively calculating the sequence design points.

A machine-learning based method and systems for protein sequence design are provided. The method includes generating a portion of a sequence and removing noise in input residue context, encoding and processing the portion of the sequence and backbone structure to obtain graph features, performing memory-efficient global graph attention layers to propagate the graph features and learn global residue interactions; and generating an entire sequence non-iteratively. The performing memory-efficient global graph attention layers includes enabling each residue node to learn residue interactions and gather information from the entire sequence while maintaining memory efficiency. The edge features of the memory-efficient global graph attention layers include interatomic distances and direction vectors.

The invention provides a DFT precoding FBMC system carrier synchronization method oriented to air-ground communication. The method comprises the following steps: acquiring FBMC symbol data to be transmitted; a special CAZAC sequence with the length of M / 4 is constructed, four synchronous sequence symbols are constructed, and corresponding sending signals of a time domain are output after the four synchronous sequence symbols and other data symbols are subjected to conjugate symmetry module, pre-FFT, IFFT subcarrier modulation and PPN multi-phase filtering; the method comprises the following steps: constructing a local sequence at a receiver end, respectively taking two continuous parts with the length of M / 4 from a received special time domain sequence signal, and respectively correlating the two continuous parts with corresponding parts of the local sequence to obtain a correlation result; and conjugating the correlation result of the first part of sequence, multiplying the conjugate with the correlation result of the second part of sequence to obtain a frequency offset measurement function, obtaining decimal frequency offset estimation according to the frequency offset measurement function, and carrying out carrier frequency offset compensation by using a frequency offset estimation value. According to the method, a synchronous training sequence design method is provided, and carrier frequency offset is estimated and corrected by using time domain signal construction characteristics of a training sequence.

Disclosed herein are methods, systems, and devices that are associated with downlink (DL) control, grant free (GF) transmission, or initial access. Particularly, disclosed herein are multi beam physical downlink control channel (PDCCH) transmission mechanisms, grant free transmissions mechanisms, demodulation reference signal (DMRS) for physical broadcast channel (PBCH) mechanisms, and DMRS sequence design for new radio-channel state information-reference signal (NR-CSI-RS) and new radio-physical downlink shared channel (PDSCH) (NR-PDSCH), among other things.

The invention belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.

Disclosed are an auxiliary pilot adaptive optimization method, apparatus and device, and a medium, which relate to the technical field of wireless communications, and solve the problems of static configuration, resource waste and single function of the existing auxiliary pilot. An auxiliary pilot frequency sequence is designed into a synchronous prefix and an estimation core section, and the synchronous prefix adopts a pseudo-random sequence with high autocorrelation and is used for timing synchronization (positioning through a sequence autocorrelation peak value) and frequency synchronization (calculating frequency offset through a phase difference) in a demodulation process; the estimation core section adopts an orthogonal sequence with a low peak-to-average ratio and is used for accurate channel estimation; the double-segment sequence is connected through a preset code pattern, and it is ensured that a demodulation end can perform synchronous analysis. By sensing the channel state in real time, parameters of the auxiliary pilot frequency are dynamically adjusted, and a synchronization function is fused, so that the demodulation performance and the resource utilization rate are improved.

The invention discloses a distributed OFDM (Orthogonal Frequency Division Multiplexing) base station cooperative sensing sequence design method. The method comprises the following steps: setting parameters required by a problem; generating a signal sequence, a Fourier transform matrix, a frequency domain subcarrier selection matrix and a zero padding matrix; generating a sidelobe level coefficient matrix and a self-cross-correlation proportionality coefficient matrix; an alternating iteration algorithm is adopted to solve the optimal solution of the problem, the waveform optimal solution is solved, the peak-to-average power ratio is limited, a matched filter is updated, energy limitation is carried out, frequency domain waveforms are updated, dual variables are updated, various residual errors, target functions and waveform self-cross-correlation characteristics are counted, iteration is finished, and an optimal waveform sequence is output; and carrying out statistics on various residual errors, objective functions, self-cross-correlation characteristics and other information. According to the method, the optimal solution of the problem is solved by adopting an alternating iteration algorithm, so that the self-cross-correlation sidelobe characteristic of the multi-station waveform is inhibited to the greatest extent, and the interference of clutters on a communication system is reduced.

The invention discloses an autoclave temperature offline simulation and online prediction method based on Hybrid KAN feature fusion. The autoclave temperature offline simulation and online prediction method comprises the steps that historical temperature time sequence data and static process parameter data of different batches are collected; performing feature screening and preprocessing, and constructing a time sequence data input sequence and a static process parameter input sequence; designing a Hybrid KAN neural network model, taking the time sequence data input sequence and the static process parameter input sequence as input, and outputting a predicted value of the temperature of the autoclave at the next moment through feature extraction and fusion; taking minimization of a loss function as a target, training the model, and obtaining an optimal model by using a structured pruning strategy; and deploying an optimal model, and realizing off-line simulation and on-line prediction of the temperature of the autoclave through rolling time window iteration multi-step prediction. The method is high in accuracy, wide in universality, small in model parameter scale, high in reasoning efficiency and suitable for autoclave temperature prediction in complex scenes.

The invention provides an industrial multi-protocol heterogeneous real-time video collaborative analysis method. The method comprises the steps that a lightweight compiler is developed, an operator dependency graph analysis module is integrated, instruction-level parallelism of a neural network processing unit is predicted through a graph neural network, and optimization intermediate representation is dynamically generated; constructing a protocol classification graph neural network model, carrying out protocol type self-identification and streaming media metadata automatic extraction, and converting a heterogeneous video stream into a standardized frame sequence; designing a multi-agent reinforcement learning model, and generating an optimal scheduling strategy through offline simulation training by taking a neural network processing unit calculation unit utilization rate, a memory bandwidth occupancy rate and a task queue depth as state spaces; predicting a video key region by adopting a hybrid network, and performing resolution downsampling on a non-key region in combination with motion vector analysis; the hardware-level scheduling adopts time slice rotation preemptive scheduling, and time slices are dynamically distributed according to the priority of an algorithm.

A sequence transmission method and an apparatus are provided, which may be applied to a downlink synchronization scenario, a random access scenario, a sensing scenario, a radar scenario, an integrated sensing and communication scenario, or the like, to increase sequence design diversity, and improve sequence design flexibility and target detection accuracy. The method includes: A transmit end apparatus determines N first sequences, and sends the N first sequences. An nth first sequence in the N first sequences is determined based on an nth second sequence in N second sequences, formula (I), am is a prime number, M is a positive integer greater than 1, and n=0,1, . . . , N−1. Each second sequence is a sequence in a Golay complementary pair GCP. The N second sequences include formula (II) first sub-sequence sets, each first sub-sequence set includes am second sub-sequence sets, each second sub-sequence set includes formula (III) second sequences, m=0,1, . . . , M−1, and a−1=1.

The invention discloses a diffusion model reasoning acceleration method based on optimal time step sequence search and knowledge distillation, which constructs a unified search space covering a time step sequence and corresponding model architecture configuration on the premise of not performing overall fine adjustment on a pre-trained diffusion model, and searches an optimal time step sequence. And designing a knowledge distillation training method based on the optimal diffusion time step sequence obtained by searching, constructing a main distillation loss function in a discrete time step subspace limited by the optimal diffusion time step sequence, and introducing adjacent time step consistency loss to relieve discrete errors introduced by a non-uniform time step span. In the reasoning stage, a teacher model is frozen, a distilled student model is combined with an efficient sampler ACDMS based on UniPC and fused with flow matching dynamic correction, finite step sampling is carried out on an optimal time step sequence, and reasoning acceleration under generation quality maintenance is achieved.

The invention discloses a cross-graph modeling three-dimensional RNA inverse folding method based on multi-structure comparison, and the method comprises the steps: carrying out the multi-structure comparison of a target RNA structure and an RNA structure database at an offline stage, obtaining a closest similar structure, carrying out the alignment processing, extracting node and edge features from the target RNA structure, and generating a training set through cross-structure coding; the training module is used for training a sequence decoder; in the online stage, through a trained sequence decoder, real-time input and cross-structure coded features are decoded, and after sequence probability distribution is obtained, a design sequence is obtained through temperature coefficient sampling. According to the method, the multi-structure comparison technology and the graph neural network are combined, and the accuracy and diversity of RNA sequence design are remarkably improved by utilizing the evolutionary conservative property of the target RNA structure and the homologous structure of the target RNA structure.

The invention relates to the technical field of pseudo-random binary sequence design, in particular to a parallel PRBS generation method and system for testing a multi-protocol high-speed serial transceiver, and the method comprises the steps: setting demand parameters of a pseudo-random binary sequence for testing the multi-protocol high-speed serial transceiver, and enabling the demand parameters to comprise an order, a bit number and a feedback polynomial; constructing a generator matrix for describing state transition of the linear feedback shift register according to the feedback polynomial; according to the bit number of the pseudo-random binary sequence, generating a mapping matrix of the pseudo-random binary sequence through the exponentiation of the generated matrix; based on the mapping matrix and the current beat linear feedback shift register state, outputting a pseudo-random binary sequence meeting the requirement parameters for testing the multi-protocol high-speed serial transceiver. According to the method, parallel generation of any PRBS can be supported, the PRBS parallel implementation logic configuration is simple, the hardware implementation complexity is low, the PRBS output throughput rate is high, and the design requirements of multi-protocol high-speed SerDes can be met.

Provided herein are methods of producing a trained model for generating peptide or protein sequence information and infilling of targeted residue spans. In some embodiments, the methods include training a model using a training dataset comprising a population of reference amino acid sequence representations in which a given amino acid sequence representation in the population is conditioned on one or more conditioning tags that provide a controllable generation of selected amino acid sequence representation types. Related methods, systems, and computer program products are also provided.

Described is an apparatus of an Evolved Node-B (eNB). The apparatus may comprise a first circuitry and a second circuitry. The first circuitry may be operable to determine a first Sounding Reference Signal (SRS) sequence and a second SRS sequence. The second circuitry may be operable to process a first Uplink (UL) transmission from the first UE incorporating the first SRS sequence over a first set of subcarrier frequencies. The second circuitry may also be operable to process a second UL transmission from the second UE incorporating the second SRS sequence over a second set of subcarrier frequencies. The second SRS sequence may comprise at least a first block that overlaps the first set of subcarrier frequencies and a second block that does not overlap the first set of subcarrier frequencies.

Techniques pertaining to mark sequence designs for enhanced long range (ELR) physical-layer protocol data unit (PPDU) in wireless communications are described. A wireless communication apparatus (e.g., a station (STA) ) generates an ELR PPDU and transmits the ELR PPDU in a wireless communication. The ELR PPDU may include a legacy preamble part, an ELR preamble part, and an ELR data symbol part. The ELR preamble part may include an ELR mark field (ELR-MARK) having two mark symbols, an ELR short training field (ELR-STF), an ELR long training field (ELR-LTF), and an ELR signal field (ELR-SIG), with the ELR-MARK transmitted after the legacy preamble part.

The invention discloses a time series prediction method based on a multi-modal enhanced large language model, which comprises the following steps: acquiring historical time series data, constructing a standardized input matrix and setting core task parameters; dividing data blocks through a sliding window mechanism, and converting the data blocks into uniform dimension features through linear embedding; a semantic prototype is generated based on a large language model vocabulary, and time sequence features and semantic features are fused through multi-head cross-attention; alternately splicing the data blocks and the corresponding semantic information, and constructing a self-multi-modal input sequence; designing a three-level structured prompt including context, task target and modal guidance, and fusing the three-level structured prompt with a multi-modal sequence; and training the lightweight model by adopting a frozen training strategy, and outputting a prediction result of a specified time step in the future. According to the method, through single-source data enhancement and prompt guidance, the time sequence reasoning capability of a small-parameter large language model is activated, high-precision prediction is guaranteed, efficient deployment is achieved, and the method adapts to long-term and short-term prediction tasks in the fields of electric power, traffic, meteorology and the like.

The application discloses an mRNA vaccine sequence design system and method based on a large language model intelligent agent, and the mRNA vaccine sequence design system based on the large language model intelligent agent comprises a user interaction and task analysis module, a task planning and intelligent agent scheduling module, an RNA design skill library and skill description module, a multi-target evaluation and closed-loop optimization module and a data and model management module. The application integrates a large language model and various mRNA sequence analysis and optimization tools, automatically completes mRNA vaccine sequence generation, evaluation and optimization for a target antigen in a computer environment, is suitable for candidate sequence design and screening of a preventive or therapeutic mRNA vaccine, improves design efficiency, and reduces the degree of dependence on manual experience and manual script arrangement.

The invention belongs to the field of wireless communication, and particularly relates to a pilot frequency sequence design method for a super-large-scale MIMO-OTFS (Multiple Input Multiple Output-OTFS), aiming at a near-field high-mobility environment, a communication system based on spherical waves is established, an optimization function is established in the communication system by taking an average non-interference measurement between minimization pilots as a target, and the pilot frequency sequence is optimized. And solving by adopting a genetic algorithm to obtain an optimal pilot frequency sequence. Compared with a traditional plane wave model, the system model based on the spherical wave has remarkable performance advantages in a near-field high-mobility environment, and the pilot frequency sequence optimization design based on the genetic algorithm obtains more superior channel estimation performance than other pilot frequency designs through the powerful self-adaptive search capability.

The present application relates to the field of polypeptide design, and provides a training method of a binding force prediction model, a polypeptide sequence design method and device, the training method comprising obtaining sequence features of a plurality of candidate polypeptide sequence samples, the sequence features comprising proportions of various amino acids and occurrence frequencies of each adjacent residue pairing group; selecting a plurality of target polypeptide sequence samples from the plurality of candidate polypeptide sequence samples according to the proportions of various amino acids and the occurrence frequencies of each adjacent residue pairing group contained in each sequence feature; simulating binding forces between each target polypeptide sequence sample and an extracellular vesicle membrane; and using at least part of the target polypeptide sequence samples and the simulated binding forces thereof as binding force labels to train a binding force prediction model, thereby obtaining a final binding force prediction model. The present application can improve the binding force prediction accuracy of the final binding force prediction model while reducing the training cost.

This invention relates to an RNA sequence design method based on isovariant geometry and graph neural networks, comprising: first, RNA three-dimensional structure data and corresponding sequences; then, after dataset encapsulation and feature extraction, vector embeddings that can be processed by graph neural networks are generated; next, the data is fed into the Equiformer+GVP model for modeling, and the model outputs the probability distribution of four bases at each nucleotide position; finally, the bases are selected according to the probability distribution to form the predicted RNA sequence. Among these methods, E(3) isovariant constraints are introduced to achieve geometric consistency expression, and multi-scale geometric feature encoding is combined to enhance the model's understanding of local and global structures, significantly improving the model's physical rationality, prediction accuracy and universality; and the three-dimensional geometric skeleton of the RNA molecule is used as the only input, eliminating the dependence on secondary structures or external contact diagrams, and learning and generation are performed directly at the three-dimensional spatial coordinate level, thereby achieving significant improvements in physical consistency, geometric completeness and generalization ability.

The invention belongs to the technical field of multidisciplinary design, and discloses a data-driven AUV (Autonomous Underwater Vehicle) multidisciplinary design optimization method considering time consumption difference of simulation units. The method comprises the steps of generating an initial sample for an optimization object and segmenting the initial sample into design samples of each subject sequence; analyzing the high-fidelity evaluation average time consumption of the design samples of each subject sequence, calculating the amplification multiplying power of the design samples of other non-time-consuming subject sequences on the basis of a total time consumption consistency principle by taking the design sample of the time-consuming subject sequence as a benchmark to construct the design samples, and performing high-fidelity evaluation on the constructed design samples to construct a subject sequence library; establishing an agent model of each subject sequence based on the subject sequence library; and segmenting the multidisciplinary system of the optimization object into each disciplinary sequence, and carrying out rapid evaluation based on the corresponding agent model. According to the method, the problem of time isomerism evaluation in the multidisciplinary design optimization process is solved, and the optimization efficiency is remarkably improved.

The invention discloses a space-frequency emission sequence design method for a dual-function radar communication system, which comprises the following steps of: constructing an optimization problem of a space-frequency emission sequence by taking mutual information optimization of multiple communication users and simultaneous radar emission beam directional diagram control of multiple targets as target functions under multiple constraint conditions; wherein the constraint conditions comprise direction mismatch interference power constraint, radar emission vector constraint and power constraint; a communication emission vector set in the fixed # imgabs0 # solution optimization problem is converted into a first optimization sub-problem; solving a radar emission vector in the optimization problem by using a fixed # imgabs1 #, and converting the radar emission vector into a second optimization sub-problem; alternately solving the first optimization sub-problem and the second optimization sub-problem until a convergence condition is met, and determining a space-frequency transmission sequence; the design is effective in the radar function and the communication function of the spatial frequency domain.

The invention relates to an efficient frequency hopping sequence construction method based on a national cryptographic algorithm SM4, belongs to the technical field of information security, and solves the problems that in the prior art, frequency hopping sequence design is low in security and insufficient in anti-interference performance and cannot meet the domestic application requirement. Comprising the following steps: generating 32 rounds of round keys based on a user key Key; receiving system clock information TOD and the round key, and generating a pseudo-random sequence of frequency hopping communication through 32 rounds of iterative operation; and converting the pseudo-random sequence to obtain a frequency hopping sequence with an adjustable frequency slot number. The frequency hopping sequence with the adjustable frequency gap number is generated, the safety is high, the efficiency is high, the anti-interference capacity is high, and the domestic application requirement is met.

The application relates to an antibody sequence design method based on a given antigen-antibody complex structure. The method comprises a. a modeling stage, which uses a deep neural network to characterize the three-dimensional local structure environment of antibody amino acid residues and the amino acid sequence, and establishes a model; b. a sequence design stage, which uses the model to iteratively optimize the amino acid sites of each CDR region by inputting the main chain structure of the target antibody. In each iteration process, the model adjusts the amino acid type according to the previous update result, gradually converges the CDR region amino acid sequence that meets the target structure and functional requirements, and completes the sequence design of the antibody. The application combines the advantages of main chain design and protein language model, focuses on the amino acid sequence design of the antibody CDR region, and significantly improves the design accuracy and functionality by introducing antigen epitope information and an iterative optimization process.

The application belongs to the technical field of multidisciplinary design, and discloses a data-driven AUV multidisciplinary design optimization method considering time consumption difference of simulation units. The method comprises the following steps: generating initial samples for an optimization object and dividing the initial samples into design samples of each discipline sequence; analyzing high-fidelity evaluation average time consumption of the design samples of each discipline sequence, calculating expansion multiples of the design samples of other non-most time-consuming discipline sequences based on the design samples of the most time-consuming discipline sequence as a benchmark to construct design samples according to a total time consumption consistency principle, performing high-fidelity evaluation on the constructed design samples to construct a discipline sequence library; establishing a proxy model of each discipline sequence based on the discipline sequence library; and dividing a multidisciplinary system of the optimization object into each discipline sequence and performing rapid evaluation based on the corresponding proxy model. The method of the application significantly improves optimization efficiency by solving the problem of evaluation time heterogeneity in the process of multidisciplinary design optimization.

Various schemes are described for long training field (LTF) sequence design for distributed tone resource units (DRUs) for wider bandwidth in wireless communications. A device (e.g., a working station (STA)) generates a DRU LTF sequence of a DRU using a predefined DRU LTF base sequence. The device transmits the DRU LTF sequence of the DRU in wireless communications of a bandwidth of 160 MHz or more.

The invention discloses a communication system of an improved parallel cascade LDPC code structure based on a single encoder, and belongs to the technical field of aviation platform laser communication. Aiming at the bottlenecks that the traditional PCGC depends on double encoders, the hardware overhead is large, the decoding delay is high and the complementarity requirement on component codes is harsh, the system adopts a single encoder architecture design: firstly, a basic verification sequence is generated through a single LDPC encoder, and differential verification constraints are constructed through interleaving and repeating operations; meanwhile, a zero syndrome and non-zero syndrome dual-check sequence design is introduced, so that the minimum distance of a code word is increased; a decoding end fuses a normalized minimum sum algorithm and a hierarchical message updating and dynamic early stop mechanism, combines channel sensing soft information construction and a self-adaptive interleaving and redundancy allocation strategy, and adapts to composite channel characteristics of an aviation platform.