In this letter, we report on the one-step synthesis of Ag@poly(m-phenylenediamine) core−shell nanoparticles (APCSNPs), carried out by direct mixing of aqueous silver nitrate and m-phenylenediamine ...solutions at room temperature. We further demonstrate the use of APCSNP as a novel fluorescent sensing platform for nucleic acid detection. In this regard, the detection of DNA is accomplished in two steps. First, APCSNP absorbs and quenches the fluorescence of dye-labeled single-stranded DNA (ssDNA) as a probe. Second, hybridizing of the probe with its target produces a double-stranded DNA (dsDNA) that detaches from APCSNP, resulting in the recovery of dye fluorescence. It suggests that this sensing system has a high selectivity down to single-base mismatch, and the results exhibit good reproducibility. Furthermore, we also demonstrate its application for the multiplex detection of nucleic acid sequences.
Resumo Fundamento A amiloidose é definida como um distúrbio caracterizado pela deposição de material proteico amiloide extracelular nos tecidos. Objetivos O N-terminal pró-peptídeo natriurético ...tipo-B (NT-proBNP) é usado para prever a amiloidose cardíaca (AC), mas seu efeito diagnóstico no comprometimento por AC ainda não é claro, especialmente em termos de especificidade e sensibilidade. Métodos Foi feita uma busca de literatura nos bancos de dados Pubmed, Embase e a biblioteca Cochrane, e o QUADAS 2 foi utilizado para avaliação da qualidade. O comando Midas no Stata 12.0 foi usado para analisar os indicadores dos sujeitos. O teste Q de Cochran e o I2 foram usados como testes de heterogeneidade, e a heterogeneidade significativa foi definida como p <0,05 e/ou I2 >50%. A análise de correlação de Spearman foi usada para avaliar o efeito de limiar, e o viés da publicação foi avaliado pelo teste de assimetria. A significância estatística foi definida em p <0,05. Resultados Como resultados, 10 conjuntos de dados de 7 estudos foram incluídos para análise, apresentando alta qualidade metodológica e pequenos vieses de confusão. A sensibilidade e a especificidade do NT-proBNP no diagnóstico do comprometimento cardíaco para pacientes com amiloidose foram 0,93 e 0,84, respectivamente. As curvas ROC também sugeriram uma validade diagnóstica alta do NT-proBNP com AUC de 0,95. Um nomograma de Fagan demonstrou que as probabilidades de NT-proBNP positivo e negativo no avanço do comprometimento por AC eram de 90% e 8%, respectivamente. O gráfico de funil de Deek não sugeriu viés significativo de publicação entre os estudos incluídos, e os resultados foram estáveis e confiáveis. Conclusões O NT-proBNP desempenha um papel positivo no diagnóstico precoce do comprometimento por AC, com alta sensibilidade e especificidade.
In this paper, a new regression and reconstruction method for process monitoring is proposed. The main contributions of the proposed approaches are as follows: 1) a new nonlinear regression algorithm ...is proposed to extract the output-relevant variation, which, compared with the conventional algorithm, builds a more direct relationship between the input and output variables; 2) the fault direction is determined by possible fault magnitude of every possible principal component; and 3) the fault is effectively diagnosed compared with the conventional kernel partial least-squares (KPLS) method. The proposed method is applied to a continuous annealing process and is compared with the KPLS method. Experiment results show that the proposed method can more effectively detect fault compared with the KPLS method. In addition, the selection of fault direction is more accurate using the proposed reconstruction algorithm compared with the KPLS reconstruction approach.
Fault Detection for Time-Varying Processes Zhang, Yingwei; Zhang, Hailong
IEEE transactions on control systems technology,
07/2014, Letnik:
22, Številka:
4
Journal Article
Recenzirano
In this brief, a new manifold learning method is proposed. Then, a process monitoring approach is proposed for handling the multimode monitoring problem in the electro-fused magnesia furnace based on ...the proposed manifold learning method. In the conventional methods, only partial common information is shared by different modes, i.e., the common eigenvectors. Compared with the conventional methods, the contributions are a new method of extracting the common subspace of different modes is proposed based on the manifold learning. The common subspace extracted by the proposed manifold learning method is shared by all different modes, and after those two different subspaces are separated, the common and specific subspace models are built and analyzed, respectively. The monitoring is carried out in the manifold subspaces.
In this paper, kernel partial least squares (KPLS) method is modified based on orthogonal independent component analysis (O-ICA). Then it is applied to quality prediction of industrial processes.
In ...ICA, the extracted components are assumed to be mutually statistically independent instead of uncorrelated. Independence is much stronger than uncorrelativity. Those extracted ICs may thus provide more informative statistical explanations and better reflect the inner properties of measurement data. However, disturbing variation can be extracted since ICA uses entropy theory to extracts high-order statistics. Hence, first, O-ICA is proposed for signal correction of non-Gaussian processes. Then KPLS is modified for quality prediction of non-Gaussian processes based on O-ICA, which is called O-ICA-KPLS. Advantages of the proposed O-ICA-KPLS are: (1) has the ability to give high-order representations for non-Gaussian data compared to original KPLS, and (2) provides more accurate statistical analysis and on-line monitoring because independent signals are corrected.
The proposed methods are applied to the quality prediction in fermentation process and Tennessee Eastman process. Applications indicate that the proposed approach effectively captures the relations in the process variables and use of O-ICA-KPLS instead of original KPLS improves the predictive ability.
In this paper, a new nonlinear process monitoring method that is based on multiway kernel independent component analysis (MKICA) is developed. Its basic idea is to use MKICA to extract some dominant ...independent components that capture nonlinearity from normal operating process data and to combine them with statistical process monitoring techniques. The proposed method is applied to the fault detection in a fermentation process and is compared with modified independent component analysis (MICA). Applications of the proposed approach indicate that MKICA effectively captures the nonlinear relationship in the process variables and show superior fault detectability, compared to MICA.
As two promising candidate techniques for the 5G mobile communication system, device-to-device (D2D) communications and full-duplex communications have drawn significant research interests. Since ...full-duplex communications are suitable for use in low transmit power scenarios to lower the residual self-interference (SI), while D2D communications work in short distance scenarios which result in low transmit power, it is natural to integrate full-duplex into D2D communications. In this paper, we investigate the power control for full-duplex D2D communications underlaying cellular networks. Specifically, we formulate the power control problem by maximizing the achievable sum-rate of the full-duplex D2D link while fulfilling the minimum rate requirement of the cellular link under the maximum transmit power constraint of the cellular user and D2D users. Two algorithms are proposed to solve the optimization problem. For the first algorithm, we convert the objective function into a concave function based on difference of convex (D. C.) structure and propose an iterative algorithm to solve the optimization problem. For the second algorithm, we consider the received signal-to-interference-plus-noise ratios (SINRs) at the D2D users are high. Based on high-SINR approximation, closed-form optimal solutions are obtained for different boundaries of the feasible region. Numerical results are presented to illustrate the effect of the channel gains and SI cancellation ability on the optimal transmit power and the achievable sum-rate of the full-duplex D2D link.
In this paper, a new approach to the optimal control with constraints is proposed to achieve a desired end product quality for nonlinear processes based on new kernel extreme learning machine (KELM). ...The contributions of the paper are as follows: (1) In existing ILC algorithm, the model was built only between manipulated input variables
U and output variables
Y without considering the state variables. However, the states variables
X
state
are important in the industrial processes, which are usually constrained. In this paper, the variables are divided into state variables
X
state
, manipulated input variables
U and output
Y in the process of modeling. Then Δ
U can be obtained by batch-to-batch iterative learning control separately. Kernel algorithm is added to ELM. (2) Constraints of state variables
X
state
and the input variables
U are considered in the current version. PSO is used to solve the optimization problem. (3) Kernel trick is introduced to improve accuracy of ELM modeling. New KELM algorithm is proposed in the current version. The input trajectory for the next batch is accommodated by searching for the optimal value through the error feedback at a minimum cost. The particle swarm optimization algorithm is used to search for the optimal value based on the iterative learning control (ILC). The proposed approach has been shown to be effective and feasible by applying bulk polymerization of the styrene batch process and fused magnesium furnace.
► The states variables
state
X are important in the industrial processes, which are usually constrained. ► The variables are divided into state variable
state
X, manipulated input variables
U and output
Y in the process of modeling. ► Then
U can be obtained by batch-to-batch iterative learning control separately. Kernel algorithm is added to ELM. ► Constraints of state variables
state
X and the input variables
U are considered in the current version. ► PSO is used to solve the optimization problem; Kernel trick is introduced.
► We combine the Kronecker production, the wavelet decomposition technique, the sliding median filter technique are combined for monitoring purpose. ► We remove the disturbances and noises. ► We ...analyze the dynamical data at different scales.
In this paper the multiscale kernel principal component analysis (MSKPCA) based on sliding median filter (SFM) is proposed for fault detection in nonlinear system with outliers. The MSKPCA based on SFM (SFM-MSKPCA) algorithm is first proposed and applied to process monitoring. The advantages of SFM-MSKPCA are: (1) the dynamical multiscale monitoring method is proposed which combining the Kronecker production, the wavelet decomposition technique, the sliding median filter technique and KPCA. The Kronecker production is first used to build the dynamical model; (2) there are more disturbances and noises in dynamical processes compared to static processes. The sliding median filter technique is used to remove the disturbances and noises; (3) SFM-MSKPCA gives nonlinear dynamic interpretation compared to MSPCA; (4) by decomposing the original data into multiple scales, SFM-MSKPCA analyze the dynamical data at different scales, reconstruct scales contained important information by IDWT, eliminate the effects of the noises in the original data compared to kernel principal component analysis (KPCA). To demonstrate the feasibility of the SFM-MSKPCA method, its process monitoring abilities are tested by simulation examples, and compared with the monitoring abilities of the KPCA and MSPCA method on the quantitative basis. The fault detection results and the comparison show the superiority of SFM-MSKPCA in fault detection.
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