In this paper, a decentralized fault diagnosis approach of complex processes is proposed based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by nonlinear ...characteristics, kernel partial least squares (KPLS) approaches have been proposed. In this paper, MBKPLS algorithm is first proposed and applied to monitor large-scale processes. The advantages of MBKPLS are: 1) MBKPLS can capture more useful information between and within blocks compared to partial least squares (PLS); 2) MBKPLS gives nonlinear interpretation compared to MBPLS; 3) Fault diagnosis becomes possible if number of sub-blocks is equal to the number of the variables compared to KPLS. The proposed methods are applied to process monitoring of a continuous annealing process. Application results indicate that the proposed decentralized monitoring scheme effectively captures the complex relations in the process and improves the diagnosis ability tremendously.
► TWEEN 20 is used as a stabilizing agent for GO as well as a reducing and immobilizing agent for Au nanoparticles. ► The hydrazine sensor based on the nanocomposites has a fast amperometric ...response. ► The detection limit of the hydrazine sensor is estimated to be 78nM. ► The nanocomposites also exhibit good catalytic activity toward 4-nitrophenol reduction.
In this paper, we develop a cost-effective and simple route for the synthesis of Au nanoparticles (AuNPs) decorated graphene oxide (GO) nanosheets using polyoxyethylene sorbitol anhydride monolaurate (TWEEN 20) as a stabilizing agent for GO as well as a reducing and immobilizing agent for AuNPs. The AuNPs assemble on the surface of TWEEN-functionalized GO by the in situ reduction of HAuCl4 aqueous solution. The morphologies of these composites were characterized by atomic force microscopy (AFM) and transmission electron microscopy (TEM). It is found that the resultant AuNPs decorated GO nanosheets (AuNPs/TWEEN/GO) exhibit remarkable catalytic performance for hydrazine oxidation. This hydrazine sensor has a fast amperometric response time of less than 3s. The linear range is estimated to be from 5μM to 3mM (r=0.999), and the detection limit is estimated to be 78nM at a signal-to-noise ratio of 3. The AuNPs/TWEEN/GO composites also exhibit good catalytic activity toward 4-nitrophenol (4-NP) reduction and the GO supports also enhance the catalytic activity via a synergistic effect.
► A new multi-scale KPLS algorithm was proposed for monitoring processes. ► MSKPLS decomposes the process measurements into separated multi-scale components using on-line wavelet transform. ► MSKPLS ...resultant multi-scale data blocks are modeled in the framework of multi-block KPLS algorithm. ► MSKPLS could provide additional scale-level information about the fault characteristics as well as more sensitive fault detection ability.
In the paper, a new multi-scale KPLS (MSKPLS) algorithm combining kernel partial least square (KPLS) and wavelet analysis is proposed for investigating the multi-scale nature of nonlinear process. The MSKPLS first decomposes the process measurements into separated multi-scale components using on-line wavelet transform, and then the resultant multi-scale data blocks are modeled in the framework of multi-block KPLS algorithm which can describe the global relationships across the entire scales as well as the localized features within each scale. To demonstrate the feasibility of the MSKPLS method, its process monitoring abilities were tested for a real industrial data set, and compared with the monitoring abilities of the standard KPLS method. The results clearly showed that the MSKPLS was superior to the standard KPLS, especially in that it could provide additional scale-level information about the fault characteristics as well as more sensitive fault detection ability.
Device-to-device (D2D) communications and full-duplex (FD) communications, which can improve the spectrum efficiency (SE) of mobile communications, have received much attention. To further improve ...the SE, some researchers have begun to integrate FD into D2D communications, which will generate more transmission modes. Since different modes have different advantages and disadvantages, the mode selection problem needs further study. In this paper, we investigate the mode selection problem for FD-enabled two-way D2D communications to improve the SE. Four transmission modes, i.e., FD underlay mode, half-duplex (HD) underlay mode, FD overlay mode and HD overlay mode, are considered. For each mode, we maximize the system SE while fulfilling the minimum rate requirements and maximum transmit power constraints for both cellular and D2D users. For FD underlay mode, the optimization problem can be transformed into a difference of convex functions (D.C.) programming and the concave-convex procedure (CCCP) algorithm can solve the problem efficiently. For HD underlay mode and FD overlay mode, we can use search plus CCCP algorithm to solve the optimization problem. For HD overlay mode, we only need to search for the optimal solution. After obtaining the maximum SE of four modes, we can select the maximum one as the optimal mode. Numerical results are presented to illustrate the effects of the channel gains and self-interference cancellation ability on the maximum SE of four modes and the transmission mode selection.
Modeling and Monitoring of Dynamic Processes Zhang, Yingwei; Chai, Tianyou; Li, Zhiming ...
IEEE transaction on neural networks and learning systems,
02/2012, Letnik:
23, Številka:
2
Journal Article
In this paper, a new online monitoring approach is proposed for handling the dynamic problem in industrial batch processes. Compared to conventional methods, its contributions are as follows: (1) ...multimodes are separated correctly since the cross-mode correlations are considered and the common information is extracted; (2) the expensive computing load is avoided since only the specific information is calculated when a mode is monitored online; and (3) after that, two different subspaces are separated, and the common and specific subspace models are built and analyzed, respectively. The monitoring is carried out in the subspace. The corresponding confidence regions are constructed according to their respective models.
The balance between gut microbiota and host is critical for maintaining host health. Although dysregulation of the gut microbiota triggers the development of various inflammatory diseases, including ...colitis, the molecular mechanism of microbiota-driven colitis development is largely unknown. Here, we found that gasdermin D (GSDMD) was activated during acute colitis. In the dextran sulfate sodium (DSS)-induced colitis model, compared to wild-type mice,
-deficient mice had less colitis severity. Mechanistically, GSDMD expression in intestinal epithelial cells (IECs), but not infiltrating immune cells, was critical for GSDMD-mediated colitis progression. Moreover, commensal
(
) largely overgrew during colitis, and then the dysregulated commensal
mediated GSDMD activation. Furthermore, the activated GSDMD promoted the release of interleukin-18 (IL-18), but not the transcript or maturation level of IL-18, which in turn mediated goblet cell loss to induce colitis development. Thus, GSDMD promotes colitis development by mediating IL-18 release, and the microbiota can mediate colitis pathogenesis through regulation of GSDMD activation. Our results provide a potential molecular mechanism by which the microbiota-driven GSDMD activation contributes to colitis pathogenesis.
•A novel alternate feeding mode for anaerobic co-digestion is proposed.•The two substrates are not premixed but alternately used according to a rule.•The operation parameters of each substrate can be ...controlled individually.•The mode can improve the methane production rate of food waste under higher load.•The mode can reduce the effect of ammonia inhibition from chicken manure.
A novel alternate feeding mode was introduced to study the possibilities of improving methane yield from anaerobic co-digestion of food waste (FW) with chicken manure (CM). Two kinds of feeding sequence (a day FW and next day CM (FM/CM), two days FM and the third day CM (FW/FM/CM)) were investigated in semi-continuous anaerobic digestion and lasted 225days, and the mono-digestions of FW and CM were used as control group, respectively. The feeding sequence of FW/CM and mono-digestion of CM were observed to fail to produce gas at hydraulic retention time (HRT) of 70days due to the ammonia inhibition, however, the mode of FW/FM/CM was proved to successfully run at HRT of 35days with a higher OLR of 2.50kgL−1d−1 and obtain a higher methane production rate of 507.58mlg−1 VS and volumetric biogas production rate of 2.1LL−1d−1.
In order to adjust the properties of polyamide 6 (PA6) and expand its application, a new strategy of introducing an aromatic imide structure into the PA6 chain through the random copolymerization ...method is reported. The diimide diacid monomer was first synthesized by the dehydration and cyclization of pyromellitic dianhydride and 6-aminocaproic acid before it reacted with 1,6-hexamethylene diamine to form poly(amide imide) (PAI) salt, and finally synthesized PA6/PAI random copolymers containing an aromatic imide structure by the random copolymerization of ε-caprolactam and PAI salt. The introduction of an aromatic imide structural unit into the PA6 chain could have a great influence on its properties. As the content of PAI increases, the crystallinity (
) and melting temperature (
) of the PA6/PAI random copolymer gradually decrease, but its glass transition temperature (
) increases obviously. When the PAI content is 20 wt%, the copolymer PA6/PAI-20 has the best comprehensive performance and not only has high thermal stabilities but also excellent mechanical properties (high strength, high modulus, and good toughness) and dielectric properties (low dielectric constant and dielectric loss). Moreover, these properties are significantly superior to those of PA6. Such high-performance PA6 random copolymers can provide great promise for the wider applications of PA6 materials.
In this paper, a multivariate data modeling approach is proposed based on modified kernel partial least squares (MKPLS) with the signal filtering method. Then it is applied to quality prediction of ...industrial processes. In the original KPLS, several disadvantages are: (1) Has to iteratively calculate until convergence of score vectors to extract one principal component. Thus, this situation will affect the computing speed and waste lots of time. (2) Has to give a limited number of iterative steps and a precision limit which will reduce the accuracy of original KPLS, when the score vectors are not convergent. (3) Contains unwanted dinal KPLS is not able to remove undesirable systematic variation in
X that is unrelated to
Y. For the above problems, a modified KPLS regression model with orthogonal-kernel projections to latent structures (O-KPLS) is proposed, which is called OKPLS-KPLS. Advantages of the proposed OKPLS-KPLS are: (1) gets score vectors directly by using the corresponded eigenvector to the largest eigenvalue instead of the iterative calculation, it will improve the computing speed, (2) does not involve the limited number of iterative steps and the precision limit, hence, it will increase the accuracy compared to that of original KPLS, and (3) removes unwanted disturbed variation through the use of data preprocessing method (O-KPLS). O-KPLS is proposed here as a nonlinear data preprocessing method that removes from
X information not correlated to
Y. Furthermore, O-KPLS solves the issue of data nonlinearity compared to orthogonal projections to latent structures (O-PLS). In this paper, the prediction performance of the proposed approach (OKPLS-KPLS) is compared to those of original KPLS and OPLS using two examples. Of the three methods, OKPLS-KPLS shows the best performance in terms of regression fitting capacity and predicting future observations of the response variable(s).
PDF
