A Novel Graph Constructor for Semisupervised Discriminant Analysis: Combined Low-Rank and k

Semisupervised Discriminant Analysis (SDA) is a semisupervised dimensionality reduction algorithm, which can easily resolve the out-of-sample problem. Relative works usually focus on the geometric relationships of data points, which are not obvious, to enhance the performance of SDA. Different from these relative works, the regularized graph construction is researched here, which is important in the graph-based semisupervised learning methods. In this paper, we propose a novel graph for Semisupervised Discriminant Analysis, which is called combined low-rank and k-nearest neighbor (LRKNN) graph. In our LRKNN graph, we map the data to the LR feature space and then the kNN is adopted to satisfy the algorithmic requirements of SDA. Since the low-rank representation can capture the global structure and the k-nearest neighbor algorithm can maximally preserve the local geometrical structure of the data, the LRKNN graph can significantly improve the performance of SDA. Extensive experiments on several real-world databases show that the proposed LRKNN graph is an efficient graph constructor, which can largely outperform other commonly used baselines

Conformal screen printed graphene 4 × 4 wideband MIMO antenna on flexible substrate for 5G communication and IoT applications

From IOP Publishing via Jisc Publications RouterHistory: received 2021-04-06, revised 2021-07-15, oa-requested 2021-07-29, accepted 2021-07-30, open-access 2021-08-20, epub 2021-08-20, ppub 2021-10Publication status: PublishedAbstract: Screen-printed graphene is integrated with multiple-input multiple-output (MIMO) technology to conquer the most concerned surge in electronic waste caused by the mass deployment of Internet of things (IoT) applications. A flexible MIMO antenna is implemented with simple fabrication process suitable for large-scale production by screen printing graphene highly conductive ink on paper substrate, ensuring high-speed 5G mass data wireless transmission without damaging the ecological environment. This environmental-friendly, low-cost, flexible and conformal MIMO antenna with orthogonal polarization diversity employs co-planar waveguide feed and planar pattern for achieving high space utilization and better integration in most scenarios, for instance, body centric networks and monitoring systems. Excellent performance has been achieved due to the high conductivity of the graphene: the fabricated antenna exhibits an average sheet resistance of 1.9Ωsq−1 . The bandwidth of the antenna ranges from 2.22 GHz to 3.85 GHz (53.71% fractional bandwidth), covering 4G long term evolution, sub-6 GHz 5G mobile communication networks, 2.5 and 3.5 GHz WiMAX, and 2.4 and 3.6 GHz WLAN. Within this range, the antenna exhibits effective radiation, also its envelope correlation coefficient remains below 0.2×10−6 , manifesting outstanding signal transmission quality in a variety of wireless networks. This work illustrates a novel aggregation of MIMO technology and graphene printing electronics, enabling cheap accessible and green MIMO antennas to be massively integrated in IoT applications

A fully coupled hydro-mechanical model for the modeling of coalbed methane recovery

Most coal seams hold important quantities of methane which is recognized as a valuable energy resource. Coal reservoir is considered not conventional because methane is held adsorbed on the coal surface. Coal is naturally fractured, it is a dual-porosity system made of matrix blocks and cleats (i.e fractures). In general, cleats are initially water saturated with the hydrostatic pressure maintaining the gas adsorbed in the coal matrix. Production of coalbed methane (CBM) first requires the mobilization of water in the cleats to reduce the reservoir pressure. Changes of coal properties during methane production are a critical issue in coalbed methane recovery. Indeed, any change of the cleat network will likely translate into modifications of the reservoir permeability. This work consists in the formulation of a consistent hydro-mechanical model for the CBM production modeling. Due to the particular structure of coal, the model is based on a dual-continuum approach to enrich the macroscale with microscale considerations. Shape factors are employed to take into account the geometry of the matrix blocks in the mass exchange between matrix and fractures. The hydro-mechanical model is fully coupled. For example, it captures the sorption-induced volumetric strain or the dependence of permeability on fracture aperture, which evolves with the stress state. The model is implemented in the finite element code Lagamine and is used for the modeling of one production well. A synthetic reservoir and then a real production case are considered. To date, attention has focused on a series of parametric analyses that can highlight the influence of the production scenario or key parameters related to the reservoir

Building Heterogeneous Multi-context Systems by Semantic Bindings

We propose a framework for heterogeneous multi-context systems, in which a special kind of semantic/implicit bridge rules are introduced. Traditional bridge rules in heterogeneous multi-context systems may make the syntax and the semantics of a context more complex, e.g., in the approach of [3] an agent may have to facing a context composed by a description logic systems and a logic program with default negations. In this paper we hide the bridge rules by semantic binding on foreign knowledge fragment, and track the semantic property of a belief/knowledge in one context by a mirror-image of it in the other context. This framework can manage heterogeneous multi-contexts in a simple way, and it keeps the original reasoning properties of the context so that the original reasoning tools are still useful

Semantic Cooperation and Knowledge Reuse by Using Autonomous Ontologies

Several proposals have been put forward to support distributed agent cooperation in the SemanticWeb, by allowing concepts and roles in one ontology be reused in another ontology. In general, these proposals reduce the autonomy of each ontology by defining the semantics of the ontology to depend on the semantics of the other ontologies. We propose a new framework for managing autonomy in a set of cooperating ontologies (or ontology space). In this framework, each language entity concept/role/individual) in an ontology may have its meaning assigned either locally with respect to the semantics of its own ontology, to preserve the autonomy of the ontology, or globally with respect to the semantics of any neighbouring ontology in which it is defined, thus enabling semantic cooperation between multiple ontologies. In this way, each ontology has a “subjective semantics” based on local interpretation and a “foreign semantics” based on semantic binding to neighbouring ontologies. We study the properties of these two semantics and describe the conditions under which entailment and satisfiability are preserved. We also introduce two reasoning mechanisms under this framework: “cautious reasoning” and “brave reasoning”. Cautious reasoning is done with respect to a local ontology and its neighbours (those ontologies in which its entities are defined); brave reasoning is done with respect to the transitive closure of this relationship. This framework is independent of ontology languages. As a case study, for Description Logic ALCN we present two tableau-based algorithms for performing each form of reasonings and prove their correctness

Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring

In general dynamic scenes, blurring is the result of the motion of multiple objects, camera shaking or scene depth variations. As an inverse process, deblurring extracts a sharp video sequence from the information contained in one single blurry image—it is itself an ill-posed computer vision problem. To reconstruct these sharp frames, traditional methods aim to build several convolutional neural networks (CNN) to generate different frames, resulting in expensive computation. To vanquish this problem, an innovative framework which can generate several sharp frames based on one CNN model is proposed. The motion-based image is put into our framework and the spatio-temporal information is encoded via several convolutional and pooling layers, and the output of our model is several sharp frames. Moreover, a blurry image does not have one-to-one correspondence with any sharp video sequence, since different video sequences can create similar blurry images, so neither the traditional pixel2pixel nor perceptual loss is suitable for focusing on non-aligned data. To alleviate this problem and model the blurring process, a novel contiguous blurry loss function is proposed which focuses on measuring the loss of non-aligned data. Experimental results show that the proposed model combined with the contiguous blurry loss can generate sharp video sequences efficiently and perform better than state-of-the-art methods

Experimental Demonstration of Printed Graphene Nanoflakes Enabled Flexible and Conformable Wideband Radar Absorbers

In this work, we have designed, fabricated and experimentally characterized a printed graphene nano-flakes enabled flexible and conformable wideband radar absorber. The absorber covers both X (8–12 GHz) and Ku (12–18 GHz) bands and is printed on flexible substrate using graphene nano-flakes conductive ink through stencil printing method. The measured results show that an effective absorption (above 90%) bandwidth spans from 10.4 GHz to 19.7 GHz, namely a 62% fraction bandwidth, with only 2 mm thickness. The flexibility of the printed graphene nano-flakes enables the absorber conformably bending and attaching to a metal cylinder. The radar cross section (RCS) of the cylinder with and without absorber attachment has been compared and excellent absorption has been obtained. Only 3.6% bandwidth reduction has been observed comparing to that of un-bended absorber. This work has demonstrated unambiguously that printed graphene can provide flexible and conformable wideband radar absorption, which extends the graphene’s application to practical RCS reductions

A Novel Approach to Oil Layer Recognition Model Using Whale Optimization Algorithm and Semi-Supervised SVM

The dataset distribution of actual logging is asymmetric, as most logging data are unlabeled. With the traditional classification model, it is hard to predict the oil and gas reservoir accurately. Therefore, a novel approach to the oil layer recognition model using the improved whale swarm algorithm (WOA) and semi-supervised support vector machine (S3VM) is proposed in this paper. At first, in order to overcome the shortcomings of the Whale Optimization Algorithm applied in the parameter-optimization of the S3VM model, such as falling into a local optimization and low convergence precision, an improved WOA was proposed according to the adaptive cloud strategy and the catfish effect. Then, the improved WOA was used to optimize the kernel parameters of S3VM for oil layer recognition. In this paper, the improved WOA is used to test 15 benchmark functions of CEC2005 compared with five other algorithms. The IWOA–S3VM model is used to classify the five kinds of UCI datasets compared with the other two algorithms. Finally, the IWOA–S3VM model is used for oil layer recognition. The result shows that (1) the improved WOA has better convergence speed and optimization ability than the other five algorithms, and (2) the IWOA–S3VM model has better recognition precision when the dataset contains a labeled and unlabeled dataset in oil layer recognition