An improved generative adversarial network with modified loss function for crack detection in electromagnetic nondestructive testing

In this paper, an improved generative adversarial network (GAN) is proposed for the crack detection problem in electromagnetic nondestructive testing (NDT). To enhance the contrast ratio of the generated image, two additional regulation terms are introduced in the loss function of the underlying GAN. By applying an appropriate threshold to the segmentation of the generated image, the real crack areas and the fake crack areas (which are affected by the noises) are accurately distinguished. Experiments are carried out to show the superiority of the improved GAN over the original one on crack detection tasks, where a real-world NDT dataset is exploited that consists of magnetic optical images obtained using the electromagnetic NDT technique.Institutional Fund Projects; Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia; National Natural Science Foundation of China; China Postdoctoral Science Foundation; Royal Society; Alexander von Humboldt Foundatio

Smart Metering System: Developing New Designs to Improve Privacy and Functionality

This PhD project aims to develop a novel smart metering system that plays a dual role: Fulfil basic functions (metering, billing, management of demand for energy in grids) and protect households from privacy intrusions whilst enabling them a degree of freedom. The first two chapters of the thesis will introduce the research background and a detailed literature review on state-of-the-art works for protecting smart meter data. Chapter 3 discusses theory foundations for smart meter data analytics, including machine learning, deep learning, and information theory foundations. The rest of the thesis is split into two parts, ‘Privacy’ and ‘Functionality’, respectively. In the ‘Privacy’ part, the overall smart metering system, as well as privacy configurations, are presented. A threat/adversary model is developed at first. Then a multi-channel smart metering system is designed to reduce the privacy risks of the adversary. Each channel of the system is responsible for one functionality by transmitting different granular smart meter data. In addition, the privacy boundary of the smart meter data in the proposed system is also discovered by introducing a data mining algorithm. By employing the algorithm, a three-level privacy boundary is concluded. Furthermore, a differentially private federated learning-based value-added service platform is designed to provide flexible privacy guarantees to consumers and balance the trade-off between privacy loss and service accuracy. In the ‘Functionality’ part, three feeder-level functionalities: load forecasting, solar energy separation, and energy disaggregation are evaluated. These functionalities will increase thepredictability, visibility, and controllability of the distributed network without utilizing household smart meter data. Finally, the thesis will conclude and summarize the overall system and highlight the contributions and novelties of this project

Microfluidics and Nanofluidics Handbook

The Microfluidics and Nanofluidics Handbook: Two-Volume Set comprehensively captures the cross-disciplinary breadth of the fields of micro- and nanofluidics, which encompass the biological sciences, chemistry, physics and engineering applications. To fill the knowledge gap between engineering and the basic sciences, the editors pulled together key individuals, well known in their respective areas, to author chapters that help graduate students, scientists, and practicing engineers understand the overall area of microfluidics and nanofluidics. Topics covered include Finite Volume Method for Numerical Simulation Lattice Boltzmann Method and Its Applications in Microfluidics Microparticle and Nanoparticle Manipulation Methane Solubility Enhancement in Water Confined to Nanoscale Pores Volume Two: Fabrication, Implementation, and Applications focuses on topics related to experimental and numerical methods. It also covers fabrication and applications in a variety of areas, from aerospace to biological systems. Reflecting the inherent nature of microfluidics and nanofluidics, the book includes as much interdisciplinary knowledge as possible. It provides the fundamental science background for newcomers and advanced techniques and concepts for experienced researchers and professionals

Simulating urban soil carbon decomposition using local weather input from a surface model

Non peer reviewe

Spatially-coordinated airborne data and complementary products for aerosol, gas, cloud, and meteorological studies: The NASA ACTIVATE dataset

The NASA Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE) produced a unique dataset for research into aerosol-cloud-meteorology interactions. An HU-25 Falcon and King Air conducted systematic and spatially coordinated flights over the northwest Atlantic Ocean. This paper describes the ACTIVATE flight strategy, instrument and complementary dataset products, data access and usage details, and data application notes

Molecular detection methods for the investigation of potential sources of Campylobacter infection

The rapid detection and identification of Campylobacter jejuni isolates to probable strain level would significantly inform the epidemiological investigation of C.jejuni infection. At the outset of this project the molecular fingerprinting techniques PFGE and AFLP were proven to be equally discriminatory for identification of outbreak strains of Campylobacter, however both techniques were time consuming and not directly applicable to potential sources of infection. Real time PCR approaches were pursued for the purpose of developing methods, which would be specific and robust for the detection of specific strains of Campylobacter. A duplex real-time polymerase chain reaction (PCR) assay for speciation of Campylobacter jejuni and C.coli using real time platforms was developed. This enabled a turnaround time of three hours and was applied for direct spéciation from sources of infection including meat samples. The development of real time PCR assays for allelic discrimination of strain associated single nucleotide polymorphisms (SNPs) based upon MLST locus alleles offered a possible approach for rapid strain detection. Single nucleotide polymorphisms defining key alleles diagnostic for six major clonal complexes were identified, following a detailed analysis of the available MLST data. Allelic discrimination assays based on real time PCR systems were designed to detect the SNPs and be specific for clonal complexes ST-21, ST-45, ST-48, ST-61 ST-206 and ST-257. SNP based assays were evaluated using panels of isolates from human infections, poultry, the environment, and the MLST reference collection, which had previously been characterized by MLST. Real time allelic discrimination assays allowed the rapid detection of C.jejuni isolates and preliminary strain identification directly from foods and environmental specimens. The ability to combine detection with the identification of epidemiologically important information beyond genus or species identification represents a major new concept in the use of nucleic acid amplification techniques for the improved detection of pathogens particularly pathogens of major public health importance such as C.jejuni