1932 results about "Small sample" patented technology

An apparatus and method are disclosed for monitoring and/or detecting concentrations of a chemical precursor in a reaction chamber. The apparatus and method have an advantage of operating in a high temperature environment. An optical emissions spectrometer (OES) is coupled to a gas source, such as a solid source vessel, in order to monitor or detect an output of the chemical precursor to the reaction chamber. Alternatively, a small sample of precursor can be periodically monitored flowing into the OES and into a vacuum pump, thus bypassing the reaction chamber.

The present invention provides a sample tank having a window for introduction of electromagnetic energy into the sample tank for analyzing a formation fluid sample down hole or at the surface without disturbing the sample. Near infrared, mid infrared and visible light analysis is performed on the sample to provide a downhole in situ or surface on site analysis of sample properties and contamination level. The onsite analysis comprises determination of gas oil ratio, API gravity and various other parameters which can be estimated by a trained neural network or chemometric equation. A flexural mechanical resonator is also provided to measure fluid density and viscosity from which additional parameters can be estimated by a trained neural network or chemometric equation. The sample tank is pressurized to obviate adverse pressure drop or other effects of diverting a small sample.

The invention relates to the field of computer vision recognition and transfer learning, and provides a small sample and zero sample image classification method based on metric learning and meta-learning, which comprises the following steps of: constructing a training data set and a target task data set; selecting a support set and a test set from the training data set; respectively inputting samples of the test set and the support set into a feature extraction network to obtain feature vectors; sequentially inputting the feature vectors of the test set and the support set into a feature attention module and a distance measurement module, calculating the category similarity of the test set sample and the support set sample, and updating the parameters of each module by utilizing a loss function; repeating the above steps until the parameters of the networks of the modules converge, and completing the training of the modules; and enabling the to-be-tested picture and the training picture in the target task data set to sequentially pass through a feature extraction network, a feature attention module and a distance measurement module, and outputting a category label with the highestcategory similarity with the test set to obtain a classification result of the to-be-tested picture.

When utilized in a flow imaging instrument, calibration beads provide a known data source that can be employed in various self-diagnostic, calibration and quality metric applications for the both the optical system of the flow imaging instrument, as well as the flow cell of the flow imaging instrument. Such data can be used to determine point spread functions associated with an imaging system, to determine a sensitivity of an imaging system, and to determine a focal point of the imaging system. Imagery collected from calibration beads can be used to determine core size and stability and TDI/flow speed synchronization. Calibration beads can be beneficially employed to enable stable system operation, even when very low sample concentration, or very small sample sizes are to be analyzed.

A system for treating a blood sample (700) having an analyte of interest comprises a strip (200) having a membrane (218), respective portions (216, 220 and 222, or 300) which are provided for receiving the sample, for lysing cells of the sample to liberate hemoglobin, and for capturing glycated hemoglobin. The latter two portions (220 and 222, or 300) of the membrane are treated with lysing and capture agents, respectively. A portion of the strip (214 or 230 or 240) is provided for holding an eluting agent and for releasing the agent upon a release condition. A system for detecting analyte comprises an optical subsystem (550) that is aligned with the strip to provide a signal corresponding to an amount of analyte, and an electronic subsystem (650) for processing the signal (560) to provide a result, such as an amount or percentage of glycated hemoglobin. To use these systems, the user simply applies a small sample (700) to the membrane (218) and closes a door (10) of the detection system over the strip (200) such that the door triggers the release of the eluting agent. No sample pre-treatment is required. The preferably handheld system (100) is a simple and convenient monitoring tool for the user, such as a diabetic patient who must monitor blood glucose on an on-going basis. While the systems are useful in the monitoring of blood glucose, they may be used for treating a sample other than blood and detecting an analyte other than an analyte in blood.

A non-volatile memory chip having SLC blocks acting as a write cache for MLC blocks for high density storage requires constant copying or folding of SLC blocks into MLC blocks. To avoid the time-consuming toggling out and in of the pages of the entire SLC block for ECC checking by a controller chip, only a small sample is checked. An optimal read point for reading the memory cells in the sample of the SLC block is dynamically determined by trying different read points so that the data is read within an error budget. Once the optimal read point is determined, it is used to read the entire SLC block without further error checking. Then the SLC block can be copied (blind folded) to the MLC block with the confidence of being within the error budget.

The present invention generally relates to a system and method that co-locates in a small flexible, configurable system and multi-level substrate sampling, rapid analysis, bio-sample storage and delivery functions to be performed on living tissues or matter obtained from living organisms. The types of the sampling may include chemical, biochemical, biological, thermal, mechanical, electrical, magnetic and optical sampling. In general, the analysis performed at the point of sampling measures the sample taken and records its value. The bio-sample storage function encapsulates a small sample of analyte and preserves it for subsequent examination or analysis, either on the organism by the system or at a remote location by an independent analysis system. Once stored, the sample can provide a record of a biological state at the precise time of sampling. The delivery at the point of sampling can include chemical, biochemical, thermal, mechanical, electrical, magnetic and optical stimuli.

The invention discloses an attention mechanism relationship comparison network model method. An attention relationship comparison network model for small sample learning under a small amount of labeled sample data is constructed. Based on a relational network architecture, the model is divided into a feature coding part, a feature combination part and a relational coding part, the feature coding module is used for extracting image feature information, and the feature combination part is used for recombining the extracted query image feature information with the training image feature information of each group to form a new combined feature map. The relation encoding module performs nonlinear metric learning of the network; by introducing an attention mechanism and a spectrum normalizationmethod into an end-to-end deep convolutional neural network model, the model has higher classification accuracy under the condition of small sample learning, the stability of a final training result of the model is improved, and the image classification accuracy of an existing model in small sample learning is improved.

A coherent laser beam combining system wherein the output of a single master oscillator is split into a plurality of signals, the signals are electronically modulated at unique frequencies. One signal is designated a reference signal while the remaining signals are passed through phase adjusters. All signals are optically amplified, aligned and passed through a beam splitter to split off a small sample that is imaged onto a photodetector. The photodetector output is fed to a signal processor that produces phase error signals that drive the phase adjusters resulting in a high-powered optically coherent output signal.

The present invention provides methods and apparatus that can manipulate, detect, and/or analyze single molecules, single small particles or single small samples of matter passing through a nanoscale gap within a nanofluidic channel of a detector.

The invention relates to a heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning, and belongs to the technical field of mobile communication. Themethod comprises the following steps: 1) taking queue stability as a constraint, combining congestion control, user association, subcarrier allocation and power allocation, and establishing a random optimization model for maximizing the total throughput of the network; 2) considering the complexity of the scheduling problem, the state space and the action space of the system are high-dimensional,and the DRL algorithm uses a neural network as a nonlinear approximation function to efficiently solve the problem of dimensionality disasters; and 3) aiming at the complexity and the dynamic variability of the wireless network environment, introducing a transfer learning algorithm, and utilizing the small sample learning characteristics of transfer learning to enable the DRL algorithm to obtain an optimal resource allocation strategy under the condition of a small number of samples. According to the method, the total throughput of the whole network can be maximized, and meanwhile, the requirement of service queue stability is met. And the method has a very high application value in a mobile communication system.

In many areas, recent developments have generated very large datasets from which it is desired to extract meaningful relationships between the dataset elements. However, to date, the finding of such relationships using prior art methods has proved extremely difficult especially in the biomedical arts. Methods for local causal learning and Markov blanket discovery are important recent developments in pattern recognition and applied statistics, primarily because they offer a principled solution to the variable/feature selection problem and give insight about local causal structure. The present invention provides a generative method for learning local causal structure around target variables of interest in the form of direct causes/effects and Markov blankets applicable to very large datasets and relatively small samples. The method is readily applicable to real-world data, and the selected feature sets can be used for causal discovery and classification. The generative method GLL-PC can be instantiated in many ways, giving rise to novel method variants. In general, the inventive method transforms a dataset with many variables into either a minimal reduced dataset where all variables are needed for optimal prediction of the response variable or a dataset where all variables are direct causes and direct effects of the response variable. The power of the invention and significant advantages over the prior art were empirically demonstrated with datasets from a diversity of application domains (biology, medicine, economics, ecology, digit recognition, text categorization, and computational biology) and data generated by Bayesian networks.

The invention provides a small-sample face recognition method which relates to a method for face recognition by means of electronic equipment. According to the face recognition method, a single-layer multi-scale convolutional neural network structure is used for performing multi-characteristic fusion and classification. The method comprises the steps of preprocessing a face image; extracting a multi-characteristic graph of the face image; extracting a DWT low-frequency subband graph, an Sobel edge characteristic graph and an LBP pattern characteristic graph; performing multi-characteristic fusion by means of the single-layer multi-scale convolutional neural network structure; and predicating a classification result by means of a Softmax classifier, thereby realizing face recognition. The small-sample face recognition method overcomes a defect of low classification recognition rate caused by a classification method in settling a small-sample face recognition problem.

The invention provides a multi-direction ship target detection and identification method in a high-resolution remote sensing image. The method is characterized by designing and realizing a multi-direction ship target detection and identification network structure under a complex scene and in a large-scale environment; automatically exploring shallow-layer and deep-layer multidimensional features and the like of a ship target based on a convolutional neural network; and realizing quick and accurate detection and recognition of multi-directional, and different scales and kinds of ship targets inthe high-resolution remote sensing image under a small sample environment. Compared with a conventional target detection method, the method is faster in detection speed and more accurate in detectionresult.

The invention discloses a Chinese financial news text classification method based on a convolutional neural network. The method is mainly divided into four parts including word vector training, text preprocessing, neural network model training and news classification. Large-scale financial news corpora are used for training to obtain a broadly common financial-category word vector model through anunsupervised learning method, the word vector is effectively imported into the training of the convolutional neural network, and the statistical information of the model is increased through a methodof dynamically regulating the word vector. The adopted convolutional neural network has a simple structure, and can show excellent performances by aiming at a small sample set, so that a Chinese financial news text classification problem can be effectively solved, and effectiveness of the convolutional neural network in processing a text classification problem can be fully proved.

Method and apparatus of spectroscopy or the like on extremely small samples in which a drop is held between two opposing surfaces by surface tension and one surface is controllably toward and away from the other. To provide and transmit exciting energy through the drop for measurement, optical fibers go through a surface and finish flush with the surface. One of the surfaces can be swung clear of the other for easy cleaning between tests. Means for determining wetted surface are provided.

The invention relates to an iterative incremental dialogue intention category identification method based on small samples. The method is based on a small sample data set. Training starts from a preliminary classification model, and along with the use of a model, the number of the preliminary models is continuously increased. Model accuracy is also gradually improved, and a training mode that a large number of samples are needed by a previous deep learning model is abandoned. According to the method, in the iterative training process, only a small number of samples are needed to train a new preliminary classification model each time. The weights of other existing historical preliminary classification models are not changed. Results of all the preliminary classification models are input into the secondary classification model for training, the calculation speed of the model cannot decrease along with increase of the number of samples, the similarity screening model can screen and removethe existing preliminary classification model, the performance is maintained under the condition that the accuracy is guaranteed. Compared with the prior art, the method is advantaged in that the number of training samples is small, the calculation performance is stable, and the model is easy to update and expand.