A Rectangular Planar Spiral Antenna for GIS Partial Discharge Detection

A rectangular planar spiral antenna sensor was designed for detecting the partial discharge in gas insulation substations (GIS). It can expediently receive electromagnetic waves leaked from basin-type insulators and can effectively suppress low frequency electromagnetic interference from the surrounding environment. Certain effective techniques such as rectangular spiral structure, bow-tie loading, and back cavity structure optimization during the antenna design process can miniaturize antenna size and optimize voltage standing wave ratio (VSWR) characteristics. Model calculation and experimental data measured in the laboratory show that the antenna possesses a good radiating performance and a multiband property when working in the ultrahigh frequency (UHF) band. A comparative study between characteristics of the designed antenna and the existing quasi-TEM horn antenna was made. Based on the GIS defect simulation equipment in the laboratory, partial discharge signals were detected by the designed antenna, the available quasi-TEM horn antenna, and the microstrip patch antenna, and the measurement results were compared

Dynamic behaviors of a delay differential equation model of plankton allelopathy

AbstractIn this paper, we consider a modified delay differential equation model of the growth of n-species of plankton having competitive and allelopathic effects on each other. We first obtain the sufficient conditions which guarantee the permanence of the system. As a corollary, for periodic case, we obtain a set of delay-dependent condition which ensures the existence of at least one positive periodic solution of the system. After that, by means of a suitable Lyapunov functional, sufficient conditions are derived for the global attractivity of the system. For the two-dimensional case, under some suitable assumptions, we prove that one of the components will be driven to extinction while the other will stabilize at a certain solution of a logistic equation. Examples show the feasibility of the main results

Fractal-based autonomous partial discharge pattern recognition method for MV motors

On-line partial discharge (PD) monitoring is being increasingly adopted to improve the asset management and maintenance of medium-voltage (MV) motors. This study presents a novel method for autonomous analysis and classification of motor PD patterns in situations where a phase-reference voltage waveform is not available. The main contributions include a polar PD (PPD) pattern and a fractal theory-based autonomous PD recognition method. PPD pattern that is applied to convert the traditional phase-resolved PD pattern into a circular form addresses the lack of phase information in on-line PD monitoring system. The fractal theory is then presented in detail to address the task of discrimination of 6 kinds of single source and 15 kinds of multi-source PD patterns related to motors, as outlined in IEC 60034. The classification of known and unknown defects is calculated by a method known as centre score. Validation of the proposed method is demonstrated using data from laboratory experiments on three typical PD geometries. This study also discusses the application of the proposed techniques with 24 sets of on-site PD measurement data from 4 motors in 2 nuclear power stations. The results show that the proposed method performs effectively in recognising not only the single-source PD but also multi-source PDs

Boosting Operational DNN Testing Efficiency through Conditioning

With the increasing adoption of Deep Neural Network (DNN) models as integral parts of software systems, efficient operational testing of DNNs is much in demand to ensure these models' actual performance in field conditions. A challenge is that the testing often needs to produce precise results with a very limited budget for labeling data collected in field. Viewing software testing as a practice of reliability estimation through statistical sampling, we re-interpret the idea behind conventional structural coverages as conditioning for variance reduction. With this insight we propose an efficient DNN testing method based on the conditioning on the representation learned by the DNN model under testing. The representation is defined by the probability distribution of the output of neurons in the last hidden layer of the model. To sample from this high dimensional distribution in which the operational data are sparsely distributed, we design an algorithm leveraging cross entropy minimization. Experiments with various DNN models and datasets were conducted to evaluate the general efficiency of the approach. The results show that, compared with simple random sampling, this approach requires only about a half of labeled inputs to achieve the same level of precision.Comment: Published in the Proceedings of the 27th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2019

Mucosal Immunization Induces a Higher Level of Lasting Neutralizing Antibody Response in Mice by a Replication-Competent Smallpox Vaccine: Vaccinia Tiantan Strain

The possible bioterrorism threat using the variola virus, the causative agent of smallpox, has promoted us to further investigate the immunogenicity profiles of existing vaccines. Here, we study for the first time the immunogenicity profile of a replication-competent smallpox vaccine (vaccinia Tiantan, VTT strain) for inducing neutralizing antibodies (Nabs) through mucosal vaccination, which is noninvasive and has a critical implication for massive vaccination programs. Four different routes of vaccination were tested in parallel including intramuscular (i.m.), intranasal (i.n.), oral (i.o.), and subcutaneous (s.c.) inoculations in mice. We found that one time vaccination with an optimal dose of VTT was able to induce anti-VTT Nabs via each of the four routes. Higher levels of antiviral Nabs, however, were induced via the i.n. and i.o. inoculations when compared with the i.m. and s.c. routes. Moreover, the i.n. and i.o. vaccinations also induced higher sustained levels of Nabs overtime, which conferred better protections against homologous or alternating mucosal routes of viral challenges six months post vaccination. The VTT-induced immunity via all four routes, however, was partially effective against the intramuscular viral challenge. Our data have implications for understanding the potential application of mucosal smallpox vaccination and for developing VTT-based vaccines to overcome preexisting antivaccinia immunity

Inverse Approach to Evaluate the Tubular Material Parameters Using the Bulging Test

Tubular material parameters are required for both part manufactory process planning and finite element simulations. The bulging test is one of the most credible ways to detect the property parameters for tubular material. The inverse approach provides more effective access to the accurate material evaluation than with direct identifications. In this paper, a newly designed set of bulging test tools is introduced. An inverse procedure is adopted to determine the tubular material properties in Krupkowski-Swift constitutive model of material deformation using a hybrid algorithm that combines the differential evolution and Levenberg-Marquardt algorithms. The constitutive model’s parameters obtained from the conventional and inverse methods are compared, and this comparison shows that the inverse approach is able to offer more information with higher reliability and can simplify the test equipment

Softened Symbol Grounding for Neuro-symbolic Systems

Neuro-symbolic learning generally consists of two separated worlds, i.e., neural network training and symbolic constraint solving, whose success hinges on symbol grounding, a fundamental problem in AI. This paper presents a novel, softened symbol grounding process, bridging the gap between the two worlds, and resulting in an effective and efficient neuro-symbolic learning framework. Technically, the framework features (1) modeling of symbol solution states as a Boltzmann distribution, which avoids expensive state searching and facilitates mutually beneficial interactions between network training and symbolic reasoning;(2) a new MCMC technique leveraging projection and SMT solvers, which efficiently samples from disconnected symbol solution spaces; (3) an annealing mechanism that can escape from %being trapped into sub-optimal symbol groundings. Experiments with three representative neuro symbolic learning tasks demonstrate that, owining to its superior symbol grounding capability, our framework successfully solves problems well beyond the frontier of the existing proposals.Comment: Published as a conference paper at ICLR 2023. Code is available at https://github.com/SoftWiser-group/Soften-NeSy-learnin

Learning with Logical Constraints but without Shortcut Satisfaction

Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the knowledge. In this paper, we present a new framework for learning with logical constraints. Specifically, we address the shortcut satisfaction issue by introducing dual variables for logical connectives, encoding how the constraint is satisfied. We further propose a variational framework where the encoded logical constraint is expressed as a distributional loss that is compatible with the model's original training loss. The theoretical analysis shows that the proposed approach bears salient properties, and the experimental evaluations demonstrate its superior performance in both model generalizability and constraint satisfaction.Comment: Published as a conference paper at ICLR 2023, and code is available at https://github.com/SoftWiser-group/NeSy-without-Shortcut