A Sliding Mode based Cascade Observer for Estimation and Compensation Controller

The sliding mode observer can estimate the system state and the unknown disturbance, while the traditional single-layer one might still suffer from high pulse when the output measurement is mixed with noise. To improve the estimation quality, a new cascade sliding mode observer containing multiple discontinuous functions is proposed in this letter. It consists of two layers: the first layer is a traditional sliding mode observer, and the second layer is a cascade observer. The measurement noise issue is considered in the source system model. An alternative method how to design the observer gains of the two layers, together with how to examine the effectiveness of the compensator based closed-loop system, are offered. A numerical example is provided to demonstrate the effectiveness of the proposed method. The observation structure proposed in this letter not only smooths the estimated state but also reduces the control consumption

A Feature Extraction Method Based on Feature Fusion and its Application in the Text-Driven Failure Diagnosis Field

As a basic task in NLP (Natural Language Processing), feature extraction directly determines the quality of text clustering and text classification. However, the commonly used TF-IDF (Term Frequency & Inverse Document Frequency) and LDA (Latent Dirichlet Allocation) text feature extraction methods have shortcomings in not considering the text’s context and blindness to the topic of the corpus. This study builds a feature extraction algorithm and application scenarios in the field of failure diagnosis. A text-driven failure diagnosis model is designed to classify and automatically judge which failure mode the failure described in the text belongs to once a failure-description text is entered. To verify the effectiveness of the proposed feature extraction algorithm and failure diagnosis model, a long-term accumulated failure description text of an aircraft maintenance and support system was used as a subject to conduct an empirical study. The final experimental results also show that the proposed feature extraction method can effectively improve the effect of clustering, and the proposed failure diagnosis model achieves high accuracies and low false alarm rates

Achieving short-cut nitrification and denitrification in modified intermittently aerated constructed wetland

This study aim to enhance nitrogen removal performance via shifting nitrogen removal pathway from nitrate to nitrite pathway. It was demonstrated that nitrite pathway was successfully and stably achieved in CWs by using modified intermittent aeration control with aeration 20 min/non-aeration 100 min and reducing DO concentration during aeration, nitrite in the effluent could accumulate to over 70% of the total oxidized nitrogen. Q-PCR analysis showed that nitrifying microbial communities were optimized under the alternating anoxic and aerobic conditions, ammonia oxidizing bacteria increased from 7.15 x 10(6) to 8.99 x 10(6) copies/g, while nitrite oxidizing bacteria decreased approximately threefold after 234 days operation. Most importantly, high nitrogen removal efficiency with ammonium removal efficiency of 94.6%, and total nitrogen removal efficiency of 82.6% could be achieved via nitrite pathway even under carbon limiting conditions. In comparison to the nitrate pathway, the nitrite pathway could improve the TN removal by about 55%. (C) 2017 Elsevier Ltd. All rights reserved.</p

Auto-analysis system for graphite morphology of grey cast iron

The current method to classify graphite morphology types of grey cast iron is based on traditional subjective observation, and it cannot be used for quantitative analysis. Since microstructures have a great effect on the mechanical properties of grey cast iron and different types have totally different characters, six types of grey cast iron are discussed and an image-processing software subsystem that performs the classification and quantitative analysis automatically based on a kind of composed feature vector and artificial neural network (ANN) is described. There are three kinds of texture features: fractal dimension, roughness and two-dimension autoregression, which are used as an extracted feature input vector of ANN classifier. Compared with using only one, the checkout correct precision increased greatly. On the other hand, to achieve the quantitative analysis and show the different types clearly, the region segmentation idea was applied to the system. The percentages of the regions with different type are reported correctly. Furthermore, this paper tentatively introduces a new empirical method to decide the number of ANN hidden nodes, which are usually considered as a difficulty in ANN structure decision. It was found that the optimum hidden node number of the experimental data was the same as that obtained using the new method

Single Diamond Structured Titania Scaffold

Single diamond (SD) network, discovered in beetles and weevils skeletons, is the holy grail in photonic materials with widest complete bandgap to date. Such structure influences the propagation of electromagnetic waves in defined frequency and is significant in photonic crystals, light-harvesting applications, optical waveguides, laser resonators, etc. However, efforts until now have not allowed a start-to-finish understanding on the production process of the unbalanced single network scaffold in natural organisms and the thermodynamical instability of SD makes it extremely difficult to be obtained by self-assembly compared to the energetically favored bicontinuous double diamond and other easily formed lattices, thus the artificial fabrication of such photonic structure in practical synthesis has last-long as a formidable challenge. Herein, we report the unprecedented bottom-up fabrication of SD titania scaffold through a one-pot co-folding scenario employing a simple diblock copolymer poly(ethylene oxide)-block-polystyrene (PEO45-b-PS241) as template and titanium diisopropoxide bis(acetylacetonate) as inorganic precursor in a mixed solvent, in which the inorganic species selectively organized in one of the skeleton enclosed by diamond minimal surface of the polymer matrix in a simultaneous assembling process. Electron crystallography investigations exhibited the tetrahedral-connected SD frameworks with space group Fd-3m in polycrystalline anatase form. Photonic bandgap calculation shows that the structure reveals a complete bandgap of 11.54 % with the dielectric contrast of titania (6.25). This work provides a straightforward solution to the complex synthesis puzzle and offers a new reference for biological relevant materials, next-generation optical devices, etc

A two-dimensional hybrid with molybdenum disulfide nanocrystals strongly coupled on nitrogen-enriched graphene via mild temperature pyrolysis for high performance lithium storage

A novel 2D hybrid with MoS₂ nanocrystals strongly coupled on nitrogen-enriched graphene (MoS₂/NGg-C₃N₄) is realized by mild temperature pyrolysis (550 °C) of a self-assembled precursor (MoS₃/g-C₃N₄–H⁺/GO). With rich active sites, the boosted electronic conductivity and the coupled structure, MoS₂/NGg₋C₃N₄ achieves superior lithium storage performance

Membership Inference Attacks by Exploiting Loss Trajectory

Machine learning models are vulnerable to membership inference attacks in which an adversary aims to predict whether or not a particular sample was contained in the target model’s training dataset. Existing attack methods have commonly exploited the output information (mostly, losses) solely from the given target model. As a result, in practical scenarios where both the member and nonmember samples yield similarly small losses, these methods are naturally unable to differentiate between them. To address this limitation, in this paper, we propose a new attack method, called TrajectoryMIA, which can exploit the membership information from the whole training process of the target model for improving the attack performance. To mount the attack in the common blackbox setting, we leverage knowledge distillation, and represent the membership information by the losses evaluated on a sequence of intermediate models at different distillation epochs, namely distilled loss trajectory, together with the loss from the given target model. Experimental results over different datasets and model architectures demonstrate the great advantage of our attack in terms of different metrics. For example, on CINIC-10, our attack achieves at least 6× higher true-positive rate at a low false-positive rate of 0.1% than existing methods. Further analysis demonstrates the general effectiveness of our attack in more strict scenarios

Nitrogen-enriched hierarchically porous carbon materials fabricated by graphene aerogel templated Schiff-base chemistry for high performance electrochemical capacitors

This article presents a facile and effective approach for synthesizing three-dimensional (3D) graphenecoupled Schiff-base hierarchically porous polymers (GS-HPPs). The method involves the polymerization of melamine and 1,4-phthalaldehyde, yielding Schiff-base porous polymers on the interconnected macroporous frameworks of 3D graphene aerogels. The as-synthesized GS-HPPs possess hierarchically porous structures containing macro-/meso-/micropores, along with large specific surface areas up to 776 m² g⁻¹ and high nitrogen contents up to 36.8 wt%. Consequently, 3D nitrogen-enriched hierarchically porous carbon (N-HPC) materials with macro-/meso-/micropores were obtained by the pyrolysis of the GS-HPPs at a high temperature of 800 °C under a nitrogen atmosphere. With a hierarchically porous structure, good thermal stability and a high nitrogen-doping content up to 7.2 wt%, the N-HPC samples show a high specific capacitance of 335 F g⁻¹ at 0.1 A g⁻¹ in 6 M KOH, a good capacitance retention with increasing current density, and an outstanding cycling stability. The superior electrochemical performance means that the N-HPC materials have great potential as electrode materials for supercapacitors

Nuclear phylogeny and insights into whole-genome duplications and reproductive development of Solanaceae plants

Solanaceae, the nightshade family, have ∼2700 species, including the important crops potato and tomato, ornamentals, and medicinal plants. Several sequenced Solanaceae genomes show evidence for whole-genome duplication (WGD), providing an excellent opportunity to investigate WGD and its impacts. Here, we generated 93 transcriptomes/genomes and combined them with 87 public datasets, for a total of 180 Solanaceae species representing all four subfamilies and 14 of 15 tribes. Nearly 1700 nuclear genes from these transcriptomic/genomic datasets were used to reconstruct a highly resolved Solanaceae phylogenetic tree with six major clades. The Solanaceae tree supports four previously recognized subfamilies (Goetzeioideae, Cestroideae, Nicotianoideae, and Solanoideae) and the designation of three other subfamilies (Schizanthoideae, Schwenckioideae, and Petunioideae), with the placement of several previously unassigned genera. We placed a Solanaceae-specific whole-genome triplication (WGT1) at ∼81 million years ago (mya), before the divergence of Schizanthoideae from other Solanaceae subfamilies at ∼73 mya. In addition, we detected two gene duplication bursts (GDBs) supporting proposed WGD events and four other GDBs. An investigation of the evolutionary histories of homologs of carpel and fruit developmental genes in 14 gene (sub)families revealed that 21 gene clades have retained gene duplicates. These were likely generated by the Solanaceae WGT1 and may have promoted fleshy fruit development. This study presents a well-resolved Solanaceae phylogeny and a new perspective on retained gene duplicates and carpel/fruit development, providing an improved understanding of Solanaceae evolution