12 results about "Clonal selection" patented technology

The invention belongs to the technical field of software development and testing, and discloses an artificial intelligence-based software development automatic test case generation system, which is characterized in that an immune heuristic case self-repairing module is adopted, defects are regarded as antigens, antibody cases capable of being self-updated are generated by using a clone selection algorithm, and a gene rearrangement mechanism is automatically triggered when an interface is changed, so that the test efficiency is improved. The details of the use case are adjusted while the core detection logic is reserved; compared with a traditional method, the mechanism can realize use case dynamic adaptation without manual intervention, the maintenance workload is remarkably reduced, and the method is particularly suitable for a complex software system with frequent iteration; the space-time coupling test scene generation engine fuses dynamic scenes such as interaction and state transition of a module and short-time operation after precise coverage login by using a space-time convolutional network; the cross-dimension holographic use case synthesis module integrates multi-source data such as codes, hardware and user behaviors through tensor decomposition to generate a composite use case; functions and performance of software in a complex scene can be comprehensively verified, and test blind areas are remarkably reduced.

Methods for characterizing genome editing, clonal expansion and associated reagents for use in such methods are disclosed herein. Some embodiments of the technology are directed to characterizing a population of cells following an engineered genomic editing event, that includes in some embodiments characterizing genomic alterations occurring at both intended and unintended genomic loci within the genome of the populations of cells. Other embodiments are directed to utilizing Duplex Sequencing for assessing a clonal selection in mixed cell populations and / or cell populations following a genomic editing event. Further examples of the present technology are directed to methods for detecting and assessing clonal expansion of cells following a genomic editing event.

The invention discloses a clone selection-based mourhua surface modeling method and related equipment, and the method constructs an'antibody-parameter 'iterative optimization framework by simulating the clone selection principle of an immune system, achieves the automatic and intelligent global search of key parameters, and improves the modeling efficiency. The defect that parameters are set by depending on artificial experience in a traditional method can be effectively overcome, and then the objectivity and repeatability of the model are ensured; through loop iteration of a series of collaborative operations such as initialization, evaluation, cloning, variation, supplementation and reselection, convergence from a preset parameter space to an optimal solution can be efficiently achieved, when a precision threshold value is met, stopping and outputting an optimal antibody are achieved, and it can be ensured that a high-precision mourhua face model is obtained in limited computing resources. According to the method, full-process automation from parameter initialization to optimal model output is realized, manual participation can be remarkably reduced, the modeling efficiency is improved, the calculation process is more stable and robust, and the method can be widely applied to the technical field of data processing.

This invention belongs to the interdisciplinary field of artificial intelligence and distributed computing, specifically a method for high-dimensional data feature selection in computing power networks based on immune federated learning. This method includes: constructing a federated immune feature space; designing a dynamic clonal selection algorithm incorporating a spatiotemporal decay factor to achieve the co-evolution and optimization of feature affinity; using a federated graph attention network to quantize and extract feature embeddings; establishing a three-level immune memory bank to enhance adaptability; designing a computing power-aware antibody diffusion scheduling mechanism to control the diffusion range based on node computing power and privacy constraints; and dynamically adjusting the immune response threshold using reinforcement learning. Ultimately, it achieves collaborative and green optimization of high-dimensional features and computing resources under privacy protection. This invention effectively solves the problems of local optima, high communication overhead, and low computing power utilization in cross-institutional high-dimensional data feature selection, significantly improving the performance and generalization ability of federated models while reducing system energy consumption.

The invention provides a computing power network data feature selection technology based on immune clone selection optimization. The computing power network data feature selection technology comprises the following steps: firstly, collecting and preprocessing network flow, equipment state and system log multi-source data in a computing power network; based on an immune clone selection theory, improved clone proliferation, adaptive Cauchy variation and conditional lethal mutation mechanisms are introduced, a multi-objective fitness function is designed, key feature subsets are screened in combination with Fisher scores, and feature redundancy and classification contribution are balanced through iterative optimization. And training an intrusion detection model by using the optimized feature subset to reduce the calculation complexity and calculation power consumption of the model. And finally, by updating the feature weight in real time and dynamically adjusting the selection strategy, adapting to the dynamically changing operation environment of the computing power network. According to the technology, the global optimization capability of the immune algorithm and the dimension reduction advantage of feature selection are fused, the efficiency and accuracy of intrusion detection of the computing power network are effectively improved, the consumption of computing power resources is reduced, and the technology adapts to the security protection requirements of the high-dimensional complex computing power network.

The present application relates to the field of intelligent coal mine gas sequence prediction, in particular to a coal and gas outburst risk identification method based on PSO-CSA, which is as follows: the present application uses clonal selection algorithm (PSO) to identify the risk of coal and gas outburst, uses particle swarm optimization algorithm (PSO) to improve the demand of CSA for the identification of coal and gas outburst risk, accelerates the convergence speed, improves the global search ability, eliminates the shock in the later stage of operation, improves the success rate of identification, and effectively identifies the outburst risk. The PSO is introduced into the mutation process of CSA, so that the mutation is no longer dependent on a large number of calculations of binary encoding and decoding, and the antibodies generated in the mutation process can also achieve the purpose of showing high affinity. A coal and gas outburst risk identification method based on PSO-CSA is established, which effectively identifies the risk of coal and gas outburst, has beneficial effects, and can be used to guide coal mine engineering practice.

Methods for characterizing genome editing, clonal expansion and associated reagents for use in such methods are disclosed herein. Some embodiments of the technology are directed to characterizing a population of cells following an engineered genomic editing event, that includes in some embodiments characterizing genomic alterations occurring at both intended and unintended genomic loci within the genome of the populations of cells. Other embodiments are directed to utilizing Duplex Sequencing for assessing a clonal selection in mixed cell populations and / or cell populations following a genomic editing event. Further examples of the present technology are directed to methods for detecting and assessing clonal expansion of cells following a genomic editing event.

A method for facilitating clonal selection includes generating a time series of images of a well containing culture medium, including a first image and a subsequent second image. The method also includes analyzing the first image with one or more processors to detect one or more candidate objects shown in the first image, and for each of the candidate objects, determining whether the object is a single cell by analyzing the image of the object using a convolutional neural network. The method further includes analyzing the second image with the processor to detect cell colonies shown in the second image, and determining whether the colonies are formed from only one cell based on whether the processor determines that at least each candidate object is a single cell. The method further includes generating output data by the processor indicating whether the colonies are formed from only one cell.

The invention relates to a hot rolling process optimization method based on a multi-objective optimization immune algorithm, and belongs to the technical field of steel rolling intellectualization. The method comprises the steps that a wire and bar production data set is acquired and preprocessed; an initialized antibody group is generated by adopting an orthogonal test design, and uniform discretization is realized; the solution space search range expansion is realized by adopting immune cloning operation; the antibody population diversity is optimized through gene recombination and clone variation by adopting immune gene operation; predicting the mechanical property corresponding to the antibody by adopting a mechanical property prediction model; dividing antibodies into dominated antibodies and non-dominated antibodies by adopting clone selection operation through dynamically adjusting relaxation parameters, dividing a non-dominated antibody group, properly screening non-dominated solutions, rejecting dense non-dominated solutions, and screening a Pareto optimal solution set; and S7, adopting an antibody population updating operation, calculating the fitness between the antibodies, and deleting the antibodies corresponding to the places with dense antibody populations. The production cost is reduced while the product quality is improved.

The invention provides a green computing power network resource demand prediction method based on an immune dot space-time model, and the method comprises the steps: constructing a six-dimensional feature space containing space coordinates (x, y) of computing power nodes, time information, a resource entropy value and task affinity; inputting the six-dimensional feature space into a space-time diagram convolutional network, and performing quantitative extraction and prediction on space-time features of node computing power requirements; adopting a dynamic clone selection algorithm to calculate the initialized antibody concentration of the node, updating the antibody concentration, and iteratively optimizing the calculation power demand result; simulating effective propagation of the computing power demand among the nodes in the computing power network based on a resource scheduling model to migrate resources among the nodes; a Q-learning strategy in reinforcement learning is used for dynamically judging nodes needing resource scheduling. The method combines an immune mechanism, space-time deep learning and reinforcement learning, improves the accuracy of computing power demand prediction and the scheduling efficiency of network resources, and achieves the intelligent optimization management of the green computing power network.

This invention relates to the fields of artificial intelligence and big data processing, specifically disclosing a method for constructing high-dimensional user profiles based on multi-source data fusion and dynamic updates. This method constructs an initial high-dimensional feature vector, introduces an immune mechanism to identify "self" and "non-self" behaviors, establishes a dynamic antibody library with a lifecycle, and combines clonal selection, high-frequency mutation, and natural apoptosis mechanisms to achieve adaptive evolution of the profile. It also constructs a cross-user immune network, utilizes common group behaviors to generate regulatory factors to correct individual biases, and integrates adversarial attack detection, version management, and viability assessment modules. Through the above technical solutions, this invention improves the accuracy, timeliness, and anti-interference capability of user profiles, providing highly reliable data support for precision marketing, intelligent recommendation, and risk prevention and control.

Clonal hematopoiesis is an age-related condition caused by somatic mutations that give a hematopoietic stem cell a clonal selective advantage. While clonal hematopoiesis is a benign condition, individuals affected by it have an increased risk of developing blood cancers, such as acute myeloid leukemia (AML). The present disclosure provides, in some aspects, methods for inhibiting clonal hematopoiesis using a senolytic agent to target senescence of bone marrow stromal cells.