关键词： 供水管网   污染源定位   用户投诉   模式识别   神经网络  

摘要： 在供水管网缺乏水质在线监测设备的城市,用户投诉信息可作为供水管网污染源定位的重要依据。研究利用BP神经网络模式识别原理,提出一种基于用户投诉的污染源定位方法。该方法通过水力水质延时模拟确定每个候选污染源对应的管网污染物扩散模式,然后考虑投诉的滞后性,在污染物到达时间的基础上附加随机滞后时间构造投诉样本。对于复杂环状供水管网,通过4个模拟污染事件表明,BP神经网络对水源污染和非水源污染均具有良好的污染源识别能力,它可利用实时用户投诉信息缩小污染源候选范围,提高城市对管网污染的应急响应能力。

关键词： 灰狼优化   递归特征消去   支持向量分类   乳腺恶性肿瘤   模式识别  

摘要： 乳腺癌是最常见的恶性肿瘤之一,也是仅次于肺癌死亡的第二大凶手。乳腺恶性肿瘤的准确迅速诊断对于癌症的治疗有着重要的意义。模式识别机器学习算法用于乳腺肿瘤的辨识可有效弥补传统诊断方法辨识精度的不足。提出一种支持向量机递归特征消去(Support Vector Machine Recursive Feature Elimination,SVM-RFE)与灰狼优化支持向量分类(Grey Wolf Optimal Support Vector Classification,GWO-SVC)的组合算法。用SVM-RFE对乳腺肿瘤数据的30条属性进行约简,将属性约简后得到的18条属性数据用于GWO-SVC学习建模,发现训练集分类准确率高达99.33%,测试集分类准确率高达99.11%,耗时只需2.12s。通过对比不同的智能算法分类结果表明,该方法具有较高的辨识精度与泛化能力。

关键词： Feature selection   high voltage cables   partial discharge   random forest  

摘要： Optimal selection of features of Partial Discharge (PD) signals recorded from defects in High Voltage (HV) cables will contribute not only to the improvement of PD pattern recognition accuracy and efficiency but also to PD parameter visualization in HV cable condition monitoring and diagnostics. This paper presents a novel Random Forest (RF)-based feature-selection algorithm for PD pattern recognition of HV cables. The algorithm is applied to feature selection of both PD signals and interference signals with the aim of obtaining the optimal features for data processing. First, the experimental data acquisition and feature extraction processes are introduced. PD signals were captured from faults created in a cable to obtain the raw PD data, and then, a set of 3500 transient PD pulses and a set of 3500 typical interference pulses were extracted, based on which 34 PD features were extracted for further processing. Furthermore, 119 two-dimensional features and 1082 three-dimensional features were generated. The paper then discusses the basic principle of the RF algorithm. Finally, RF-based feature selection was implemented to determine the optimal features for PD pattern recognition. The results were obtained and evaluated with the Back Propagation Neural Network and Support Vector Machine. Results show that the proposed RF-based method is effective for PD feature selection of HV cables with the potential for application to additional HV power apparatus.

关键词： Geophysical methods   Image processing   Neural networks, fuzzy logic  

摘要： 3-D seismic interpretation is essential for subsurface characterization and exploration. Many existing interpretation techniques have been developed for identifying certain seismic features (e.g. faults and salt domes) that are important for depositional facies and hydrocarbon system analysis, but at the expense of deemphasizing or removing other features. Interpreting all the important seismic patterns simultaneously is a long-time issue, although many authors have attempted with the aid of multiple seismic attributes and classification schemes. To resolve this problem, this study proposes developing a seismic texture network (StNet) for automated pattern recognition, classification, and interpretation from 3-D seismic data. The workflow begins with constructing a preliminary seismic texture data set (StData-12), which tentatively categorizes 12 commonly observed seismic patterns based on their signal texture of important geological features such as faults, salt bodies, gas chimneys, depositional facies and stratigraphic features. Then we build the StNet using the state-of-the-art architecture of fully convolutional neural networks and train it based on the constructed data set StData-12. For the seismic data sets we have tested, the StNet is proven to be capable of automatically recognizing and annotating the 12 defined seismic patterns in real time, which allows interpreters to quickly identify the important seismic features simultaneously from a seismic volume. Moreover, we demonstrate that the StNet can be utilized for deriving more task-oriented networks, such as a fault-detection neural network. It is concluded that the proposed StNet as an automated process for machine cognitive data analysis should have broad applications in geological and environmental sciences, mining and drilling engineering, and hydrocarbon exploration. In-depth seismic texture analysis and interdisciplinary collaboration are expected in the future for both enriching the StData-12 and c

关键词： Technical state   circuit breaker   short circuit currents   technical diagnostics   machine learning   pattern recognition   gradient boosted trees algorithm   sample  

摘要： This paper is devoted to improving the assessment of the technical state (health index) of power circuit breakers through the use of machine learning, which allows to formalize expert knowledge, to identify implicit correlations and dependencies, and to automate the decision-making process under uncertainty. The paper proves the possibility of using machine learning based on decision trees to solve the task of assessing the power circuit breakers technical state and solves the problem of determining its optimal parameters and the training sample. The authors of the study carried out approbation of the developed model for assessing the technical state of breakers based on analyzing a large area of electrical networks in the Sverdlovsk Region, the Russian Federation, which proved its efficiency. The necessity of taking into account short-circuit currents when recognizing the pattern of the power breakers technical state is substantiated.

关键词： Imaging   Biological tissue   Scattering  

摘要： Because vascular patterns inside skin tissue are unique to individuals and impossible to counterfeit, they can be used for biometric identification. However, due to light scattering by skin tissue, obtaining a clear image without imaging the transmitted light using a lens is difficult, which in turn results in a large detection system. In this study, we present a novel method for imaging vascular patterns that uses image scanning with reflected light and no lenses. We numerically confirm that the image scanning method produces a clearer image than imaging methods that do not employ the scanning method. In addition, using a smaller light source and detector produces a clearer image of vascular patterns. At a scan pixel size of 2 mu m, we determined that the degree of cross-correlation was G = 0.74.

关键词： Pattern recognition   HD-sEMG   Spatial features   Spatial filtering   Sequentiel and propotionnal control   Linear regression   Assistive robotics  

摘要： To enable an efficient alternative control of an assistive robotic arm using electromyographic (EMG) signals, the control method must simultaneously provide both the direction and the velocity. However, the contraction variations of the forearm muscles, used to proportionally control the device's velocity using a regression method, can disturb the accuracy of the classification used to estimate its direction at the same time. In this paper, the original set of spatial features takes advantage of the 2D structure of an 8 x 8 high-density surface EMG (HD-sEMG) sensor to perform a high accuracy classification while improving the robustness to the contraction variations. Based on the HD-sEMG sensor, different muscular activity images are extracted by applying different spatial filters. In order to characterize their distribution specific to each movement, instead of the EMG signals' amplitudes, these muscular images are divided in sub-images upon which the proposed spatial features, such as the centers of the gravity coordinates and the percentages of influence, are computed. These features permits to achieve average accuracies of 97% and 96.7% to detect respectively 16 forearm movements performed by a healthy subject with prior experience with the control approach and 10 movements by ten inexperienced healthy subjects. Compared with the time-domain features, the proposed method exhibits significant higher accuracies in presence of muscular contraction variations, requires less training data and is more robust against the time of use. Furthermore, two fine real-time tasks illustrate the potential of the proposed approach to efficiently control a robotic arm. (C) 2019 Elsevier Ltd. All rights reserved.

摘要： Background: Constitutional loss of function (LOF) single nucleotide polymorphisms (SNPs) in pattern recognition receptors FPR1, TLR3, and TLR4 have previously been reported to predict oxaliplatin benefit in colorectal cancer. Confirmation of this association could substantially improve patient stratification. Methods: We performed a retrospective biomarker analysis of the Short Course in Oncology Therapy (SCOT) and COIN/COIN-B trials. Participant status for LOF variants in FPR1 (rs867228), TLR3 (rs3775291), and TLR4 (rs4986790/rs4986791) was determined by genotyping array or genotype imputation. Associations between LOF variants and disease-free survival (DFS) and overall survival (OS) were analyzed by Cox regression, adjusted for confounders, using additive, dominant, and recessive genetic models. All statistical tests were two-sided. Results: Our validation study populations included 2929 and 1948 patients in the SCOT and COIN/COIN-B cohorts, respectively, of whom 2728 and 1672 patients had functional status of all three SNPs determined. We found no evidence of an association between any SNP and DFS in the SCOT cohort, or with OS in either cohort, irrespective of the type of model used. This included models for which an association was previously reported for rs867228 (recessive model, multivariable-adjusted hazard ratio [HR] for DFS in SCOT = 1.19, 95% confidence interval [CI] = 0.99 to 1.45, P = .07;HR for OS in COIN/COIN-B = 0.92, 95% CI = 0.63 to 1.34, P = .66), and rs4986790 (dominant model, multivariable-adjusted HR for DFS in SCOT = 0.86, 95% CI = 0.65 to 1.13, P = .27;HR for OS in COIN/COIN-B = 1.08, 95% CI = 0.90 to 1.31, P = .40). Conclusion: In this prespecified analysis of two large clinical trials, we found no evidence that constitutional LOF SNPs in FPR1, TLR3, or TLR4 are associated with differential benefit from oxaliplatin. Our results suggest these SNPs are unlikely to be clinically useful biomarkers.

关键词： photoelectrochemistry   wide bandgap semiconductor   artificial neuron   in materio computing   neuromorphic computing  

摘要： Materials exhibiting memory or those capable of implementing certain learning schemes are the basic building blocks used in hardware realizations of the neuromorphic computing. One of the common goals within this paradigm assumes the integration of hardware and software solutions, leading to a substantial efficiency enhancement in complex classification tasks. At the same time, the use of unconventional approaches towards signal processing based on information carriers other than electrical carriers seems to be an interesting trend in the design of modern electronics. In this context, the implementation of light-sensitive elements appears particularly attractive. In this work, we combine the abovementioned ideas by using a simple optoelectronic device exhibiting a short-term memory for a rudimentary classification performed on a handwritten digits set extracted from the Modified National Institute of Standards and Technology Database (MNIST)(being one of the standards used for benchmarking of such systems). The input data was encoded into light pulses corresponding to black (ON-state) and white (OFF-state) pixels constituting a digit and used in this form to irradiate a polycrystalline cadmium sulfide electrode. An appropriate selection of time intervals between pulses allows utilization of a complex kinetics of charge trapping/detrapping events, yielding a short-term synaptic-like plasticity which in turn leads to the improvement of data separability. To the best of our knowledge, this contribution presents the simplest hardware realization of a classification system capable of performing neural network tasks without any sophisticated data processing.

关键词： 液滴  

ISBN: (纸本)9787563558629

摘要： 本书共12章。介绍了液滴分析技术、液滴分析仪、液滴指纹图的滤波处理等。