A highly sensitive ratiometric near-infrared nanosensor based on erbium-hyperdoped silicon quantum dots for iron(Ⅲ) detection

Ratiometric fluorescent detection of iron(Ⅲ)(Fe^(3+))offers inherent self-calibration and contactless analytic capabilities.However,realizing a dual-emission near-infrared(NIR)nanosensor with a low limit of detection(LOD)is rather challenging.In this work,we report the synthesis of water-dispersible erbium-hyperdoped silicon quantum dots(Si QDs:Er),which emit NIR light at the wavelengths of 810 and 1540 nm.A dual-emission NIR nanosensor based on water-dispersible Si QDs:Er enables ratiometric Fe^(3+)detection with a very low LOD(0.06μM).The effects of pH,recyclability,and the interplay between static and dynamic quenching mechanisms for Fe^(3+)detection have been systematically studied.In addition,we demonstrate that the nanosensor may be used to construct a sequential logic circuit with memory functions.

Sequential Pattern Technology for Visual Fire Detection

Visual fire detection technologies can detect fire and alarm warnings earlier than conventional fire detectors. This study proposes an effective visual fire detection method that combines the statistical fire color model and sequential pattern mining technology to detect fire in an image. Furthermore, the proposed method also supports real-time fire detection by integrating adaptive background subtraction technologies. Experimental results show that the proposed method can effectively detect fire in test images and videos. The detection accuracy of the proposed hybrid method is better than that of Celik＇s method.

A method for constraining the end effect of EMD based on sequential similarity detection and adaptive filter

Aimed at the problem of the end effect when using empirical mode decomposition(EMD),a method for constraining the end effect of EMD is proposed based on sequential similarity detection and adaptive filter.The method divides the signal into many wavelets,and it changes the initial wavelet length to select the best initial wavelet that has the minimum error and maximum number of matching seed wavelets,and the wavelet slopes are used for pre-matching and secondary matching to speed up the matching speed.Then,folded self-adaptive threshold is used to select multiple seed wavelets,and finally the end waveform is predicted and expanded according to the adaptive filter method.The proposed method is used to analyze the non-stationary nonlinear simulation signal and experimental signal,and it is compared with the mirror extension and RBF extension methods.The orthogonality index and similarity index of the EMD results of the extended signal after the proposed method are better than those of the other methods.The results show that the proposed method can better constrain the end effect,and has certain validity,accuracy and stability in solving the end effect problem.

A New Sequential Detection Based on Hopfield Neural Network in Frequency Selective Fading Channels

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Sequential Detection Based on Hopfieid Neural Network

A sequence detector based on Hopfield Neural network(HNN) is presented, which is used to estimate the transmitted sequences from the received signals in mobile communications. In order to avoid the convergence of HNN in local minima, a decreasing step algorithm (DSA) is proposed to search the optimum sequence quickly on the basis of the traditional simulated annealing (SA) algorithm. Computer simulation results show that the new HNN detector provides almost the same performance as that of the Viterbi detector while needs less computations and memory capacity, thus it is more feasible in hardware implementation and long constraint convolutional decoding.

Sequential Tests for the Detection of Voice Activity and the Recognition of Cyber Exploits

We consider the problem of automated voice activity detection (VAD), in the presence of noise. To attain this objective, we introduce a Sequential Detection of Change Test (SDCT), designed at the independent mixture of Laplacian and Gaussian distributions. We analyse and numerically evaluate the proposed test for various noisy environments. In addition, we address the problem of effectively recognizing the possible presence of cyber exploits in the voice transmission channel. We then introduce another sequential test, designed to detect rapidly and accurately the presence of such exploits, named Cyber Attacks Sequential Detection of Change Test (CA-SDCT). We analyse and numerically evaluate the latter test. Experimental results and comparisons with other proposed methods are also presented.

Sequential Detection of Digital Watermarking

The paper analyzed a new watermarking detection paradigm including double detection thresholds based on sequential hypothesis testing. A joint design of watermarking encoding and detection was proposed. The paradigm had good immunity to noisy signal attacks and high detection probability. Many experiments proved that the above algorithm can detect watermarks about 66% faster than popular detectors, which could have significant impact on many applications such as video watermarking detection and watermark-searching in a large database of digital contents.

Change-Point Detection for General Nonparametric Regression Models

A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.

Fast Digital Correlation Calculation Method in Extended Beacon Wavefront Detection

On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.

Speech detection method based on a multi-window analysis

Aiming at the poor performance of speech signal detection at low signal-to-noise ratio(SNR),a method is proposed to detect active speech frames based on multi-window time-frequency(T-F)diagrams.First,the T-F diagram of the signal is calculated based on a multi-window T-F analysis,and a speech test statistic is constructed based on the characteristic difference between the signal and background noise.Second,the dynamic double-threshold processing is used for preliminary detection,and then the global double-threshold value is obtained using K-means clustering.Finally,the detection results are obtained by sequential decision.The experimental results show that the overall performance of the method is better than that of traditional methods under various SNR conditions and background noises.This method also has the advantages of low complexity,strong robustness,and adaptability to multi-national languages.

Modeling of Non-Coherent Sequential Acquisition Process for DS/SS Signals

A modified non-coherent sequential detection decision logic based on continuous accumulation to achieve fast PN code acquisition is proposed. To simplify the design and analysis, the equivalent relationship between the likelihood ratio of the current sample and that of all the previous samples is deduced. The scheme is proved to be an optimum sequential detection under certain assumptions. Because the average sample number (ASN) can not be calculated through the methods applied to the conventional sequential detection, an algorithm is also provided, which can estimate both the probability density function (pdf) and the upper threshole of ASN. The desired probabilities of false alarm and detection, as well as faster PN code acquisition compared to the conventional sequential detection can be achieved by employing this structure . In addition, Rayeigh-faded reception case is also taken into consideration. Performances of the proposed schemes are obtained, which suggest that the proposed non-coherent sequential detection is more desirable.

A Particle Filter of Blind Equalization and Multiuser Detection in Asynchronous DS/CDMA Systems

The particle filter （PF） is proposed to be the asynchronous direct-sequence code-division multiple-access （DS/CDMA） multiuser detector without knowing the channel state information. The PF performs symbol detection according to the joint posterior density probability of simulated particles including relative delays, fading gains and symbols via sequential importance sample and resample. A simplified scheme is also proposed by separating the indepent relative delays and fading with symbols. These parameters are modeled as the extended aggressive processes and estimated by the Kalman filter, so as to provide their arbitrary distribution for symbol detection. Simulation results show that the bit error rate of the PF is less than conventional detectors. Moreover, the complexity of PF is moderate comparable to other nonlinear suboptimal approaches.

Hybrid Optimization of Support Vector Machine for Intrusion Detection

Support vector machine (SVM) technique has recently become a research focus in intrusion detection field for its better generalization performance when given less priori knowledge than other soft-computing techniques. But the randomicity of parameter selection in its implement often prevents it achieving expected performance. By utilizing genetic algorithm (GA) to optimize the parameters in data preprocessing and the training model of SVM simultaneously, a hybrid optimization algorithm is proposed in the paper to address this problem. The experimental results demonstrate that it’s an effective method and can improve the performance of SVM-based intrusion detection system further.

基于高维多目标序贯三支决策的恶意代码检测模型

针对传统基于二支决策的恶意代码检测方法在面对动态环境中的复杂海量数据时,没有考虑在信息不充足条件下进行决策产生影响的问题,本文提出了一种基于卷积神经网络的序贯三支决策恶意代码检测模型。通过卷积神经网络对样本数据进行特征提取并构建多粒度特征集,引入序贯三支决策理论对恶意代码进行检测。为改善检测模型整体性能,避免阈值选取的主观性,本文在上述模型的基础上,同时考虑模型的综合分类性能、决策效率和决策风险代价建立高维多目标序贯三支决策模型,并采用高维多目标优化算法对模型进行求解。仿真结果表明,模型在保证检测性能的同时,有效地提升了决策效率,降低了决策时产生风险代价,更好地拟合了真实动态检测环境。

基于特征提取和最优加权集成策略的风机叶片结冰故障检测

针对风机叶片结冰检测中现有集成方法不能充分发挥不同个体分类器优势的问题,提出了一种基于特征提取和最优加权集成学习的叶片结冰检测模型。首先,用堆叠降噪自动编码器提取结冰关联特征后,考虑不同单一分类器在二分类应用中的表现及其差异,选择随机森林、极限梯度提升树、轻量梯度提升机、K-近邻算法作为个体学习器,并用贝叶斯算法对其进行超参数优化。然后提出基于序列二次规划的最优加权集成策略对叶片状态进行判别。最后利用金风科技提供的15号和21号风机的历史数据进行了仿真实验,结果表明:所提出的检测模型与个体学习器及其他集成模型相比多项指标均有所提升,准确度达到了99.2%,在结冰检测方面具有一定的有效性。

基于相似性原理的燃气电厂给水泵异常检测方法研究

设备运行监盘是燃气电厂重要的工作内容,提前识别设备运行中可能出现的异常并进行可靠预警,对保障电厂安全平稳运行至关重要。文章以燃气电厂给水泵为研究对象,基于大量运行历史数据,开发了一种基于相似性原理(SBM)和序列概率比检验(SPRT)的异常检测算法模型。通过建立当前观测向量与代表设备健康运行状态矩阵之间的相似度模型,得到各特征当前时刻预估值,并对不同时刻下各特征的模型预测残差进行均值检验,识别设备运行异常。结果表明,该预警方法可以使参数在偏离正常运行工况还未超过控制系统报警值之前,发出预警,弥补了原有工控系统固定值报警以及人工监盘导致的感知滞后、监视不到位的问题,为设备预测性维护提供技术支撑。

基于声化学微试剂快速检测方法研究

针对高锰酸盐检测存在试剂用量大,检测周期长等问题,基于超声液体声化反应机理,应用超声辅助消解技术和顺序注射分析技术(SIA),提出了一种高锰酸盐微试剂快速检测方法。基于超声辅助高锰酸盐溶液消解原理,设计消解池结构,采用最小二乘法建立高锰酸盐回归模型。结果表明,超声辅助消解法曲线相关性优于光谱法。基于高锰酸盐指数检测仪行业标准,量程漂移与零点漂移在±5%以下,系统重复性误差为2.51%以内。实验表明,该方法具有一定的应用价值。

基于多算法融合的锂离子电池故障诊断方法

锂离子电池的安全和热失控问题,限制了其在各领域的广泛应用。为了更准确地检测电池异常情况中的内短路与开路故障,提出一种基于多算法融合的锂离子电池故障诊断方法。首先,采用逐次变分模态分解(sequential variational mode decomposition,SVMD)计算每个电池单体的最大电压波动,并通过箱线图和离群点检测来识别异常单体。其次,根据SVMD得到的电压均值差的绝对值,结合斜率计算和统计分析,检测潜在的内短路或开路故障。最后,利用实际锂离子电池的热失控数据对所提方法进行验证。结果表明,该方法可以在热失控发生前55 min进行预警,并能精确识别内短路或开路故障单体,具有较高的可靠性和工程应用价值。

PLC技术在自动化控制系统中的应用

阐述PLC技术的特点,探讨PLC技术在顺序控制、开关量控制、细节检测设计等方面的实际应用。其中顺序控制能够按照特定顺序执行操作,实现自动化流程,提高生产效率。开关量控制可以控制开关设备的状态,实现设备的启停和状态切换。

Sequential Similarity Detection Algorithm Based on Image Edge Feature

： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.