Development of a stand alone inverter efficiency test setup

Murdoch University's School of Engineering and Information Technology has a plan to develop the stand-alone inverter efficiency test setup for the laboratory exercise of the unit ENG421 Renewable Energy Systems Engineering. The laboratory exercise, which identified Inverters for Stand-Alone PV Systems, aims at helping students gain an understanding of the principles of operation, efficiencies and output current and voltage wave shapes at various loadings of two inverter types used in stand-alone PV systems. This report focuses on the development of the previous setup of ENG421 laboratory at Building 190 on the Murdoch University Campus. The aim of the development is to work out some issues with the existing system including an ageing battery bank which is expensive to replace and the replacement of the inverters that they are expected to meet Australian Standards: AS5603 Stand-alone Inverters - Performance requirements and AS4763:2011 Safety of Portable Inverters. Moreover, the aim is to determine the uncertainty in measurement by undertaking the uncertainty analysis of the efficiency test results. The stand-alone inverter efficiency test setup consists of power source, digital power analyzer, hall effect sensor circuit to supply isolated signals for viewing AC voltage and current, differential probe, oscilloscope, various load banks and two types of stand-alone inverters (sine wave inverter and modified square wave inverter) for comparing efficiency test results and output waveforms. The design work and implementation of the development is complete now. The Agilent DC power supply (0-60V, 0-11A, 6.6kW) is used to replace the battery bank due to its good ability to simulate the batteries and high-quality performance during long period. Moreover, the Protek multimeters of previous setup are replaced by YOKOGAWA WT2030 digital power meter to achieve more accurate measurements. Two stand-alone inverters are selected and used in the new setup so far, they are Selectronic Sine Wave Inverter 350VA and Suntron Power Inverter 350VA (modified square wave inverter). However, all of these inverters were manufactured before 2011. To meet the requirement of AS4763:2011 Safety of Portable Inverters, a new stand-alone inverter manufactured after 2011 should be purchased for the setup in the future work. In this project, several specific tasks were also carried out including the review of the previous work, redesign and implementation of the test setup, documentation and analysis of the stand-alone inverter efficiency test, and the determination of the uncertainty in measurement. Also, the background information relating to the stand-alone inverter is provided and discussed. The maximum efficiency of Selectronic 350VA sine wave inverter is 88.8% when there is 104 watts loading. The maximum conversion efficiency of Suntron 350VA modified square wave inverter is higher which is 92.9% when there is approximately 150 watts loading. The final expanded uncertainty for measurement of the peak efficiency point of Suntron 350VA modified square wave inverter is 0.072%. The work is completed now and ready to be used in the laboratory exercise of unit ENG421 Renewable Energy Systems Engineering. But the test setup requires new stand-alone inverters and improvement of measuring equipment for future work. The ultimate purpose of the test setup is to provide future students a safe, informative and reasonable experiment exercise

Two Solar Tornadoes Observed with the Interface Region Imaging Spectrograph

The barbs or legs of some prominences show an apparent motion of rotation, which are often termed solar tornadoes. It is under debate whether the apparent motion is a real rotating motion, or caused by oscillations or counter-streaming flows. We present analysis results from spectroscopic observations of two tornadoes by the Interface Region Imaging Spectrograph. Each tornado was observed for more than 2.5 hours. Doppler velocities are derived through a single Gaussian fit to the Mg~{\sc{ii}}~k~2796\AA{}~and Si~{\sc{iv}}~1393\AA{}~line profiles. We find coherent and stable red and blue shifts adjacent to each other across the tornado axes, which appears to favor the interpretation of these tornadoes as rotating cool plasmas with temperatures of $10^4$ K-$10^5$ K. This interpretation is further supported by simultaneous observations of the Atmospheric Imaging Assembly on board the Solar Dynamics Observatory, which reveal periodic motions of dark structures in the tornadoes. Our results demonstrate that spectroscopic observations can provide key information to disentangle different physical processes in solar prominences.Comment: 14 figures, accepted by Ap

Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years

Abstract With the rapid development of urbanization and population migration, since the 20th century, the natural and eco-environment of coastal areas have been under tremendous pressure due to the strong interference of human response. To objectively evaluate the coastal eco-environment condition and explore the impact from the urbanization process, this paper, by integrating daytime remote sensing and nighttime remote sensing, carried out a quantitative assessment of the coastal zone of China in 2000–2019 based on Remote Sensing Ecological Index (RSEI) and Comprehensive Nighttime Light Index (CNLI) respectively. The results showed that: 1) the overall eco-environmental conditions in China's coastal zone have shown a trend of improvement, but regional differences still exist; 2) during the study period, the urbanization process of cities continued to advance, especially in seaside cities and prefecture-level cities in Jiangsu and Shandong, which were much higher than the average growth rate; 3) the Coupling Coordination Degree (CCD) between the urbanization and eco-environment in coastal cities is constantly increasing, but the main contribution of environmental improvement comes from non-urbanized areas, and the eco-environment pressure in urbanized areas is still not optimistic. As a large-scale, long-term series of eco-environment and urbanization process change analysis, this study can provide theoretical support for mesoscale development planning, eco-environment condition monitoring and environmental protection policies from decision-makers

Boosting the Adversarial Transferability of Surrogate Models with Dark Knowledge

Deep neural networks (DNNs) are vulnerable to adversarial examples. And, the adversarial examples have transferability, which means that an adversarial example for a DNN model can fool another model with a non-trivial probability. This gave birth to the transfer-based attack where the adversarial examples generated by a surrogate model are used to conduct black-box attacks. There are some work on generating the adversarial examples from a given surrogate model with better transferability. However, training a special surrogate model to generate adversarial examples with better transferability is relatively under-explored. This paper proposes a method for training a surrogate model with dark knowledge to boost the transferability of the adversarial examples generated by the surrogate model. This trained surrogate model is named dark surrogate model (DSM). The proposed method for training a DSM consists of two key components: a teacher model extracting dark knowledge, and the mixing augmentation skill enhancing dark knowledge of training data. We conducted extensive experiments to show that the proposed method can substantially improve the adversarial transferability of surrogate models across different architectures of surrogate models and optimizers for generating adversarial examples, and it can be applied to other scenarios of transfer-based attack that contain dark knowledge, like face verification. Our code is publicly available at \url{https://github.com/ydc123/Dark_Surrogate_Model}.Comment: Accepted at 2023 International Conference on Tools with Artificial Intelligence (ICTAI

Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products

As an important tropospheric trace gas and precursor of photochemical smog, the accumulation of NO2 will cause serious air pollution. China, as the largest developing country in the world, has experienced a large amount of NO2 emissions in recent decades due to the rapid economic growth. Compared with the traditional air pollution monitoring technology, the rapid development of the remote sensing monitoring method of atmospheric satellite has gradually become the critical technical means of global atmospheric environmental monitoring. To reveal the NO2 pollution situation in China, based on the latest NO2 products from Sentinel-5P TROPOMI, the spatial\u2013temporal characteristics and impact factors of troposphere NO2 column concentration of mainland China in the past year (February 2018 to January 2019) were analyzed on two administrative levels for the first time. Results show that the monthly fluctuation of tropospheric NO2 column concentration has obvious characteristics of \u201chigh in winter and low in summer\u201d, while the spatial distribution forms a \u201chigh in East and low in west\u201d pattern, bounded by Hu Line. The comparison of Coefficient of Variation (CV) and spatial autocorrelation models at two kinds of administrative scales indicates that although the spatial heterogeneity of NO2 column concentration is less affected by the observed scale, there is a \u201cdelayed effect\u201d of about one month in the process of NO2 column concentration fluctuation. Besides, the impact factors analysis based on Spatial Lag Model (SLM) and Geographic Weighted Regression (GWR) reveals that there is a positive correlation between nighttime light intensity, the secondary and tertiary industries proportion and NO2 column concentration. Furthermore, for regions with serious NO2 pollution in North China Plain, the whole society electricity consumption and vehicle ownership also play a positive role in increasing the NO2 column concentration. This study will enlighten the government and policy makers to formulate policies tailored to local conditions, to more effectively implement NO2 emission reduction and air pollution prevention

Unsupervised Echocardiography Registration through Patch-based MLPs and Transformers

Image registration is an essential but challenging task in medical image computing, especially for echocardiography, where the anatomical structures are relatively noisy compared to other imaging modalities. Traditional (non-learning) registration approaches rely on the iterative optimization of a similarity metric which is usually costly in time complexity. In recent years, convolutional neural network (CNN) based image registration methods have shown good effectiveness. In the meantime, recent studies show that the attention-based model (e.g., Transformer) can bring superior performance in pattern recognition tasks. In contrast, whether the superior performance of the Transformer comes from the long-winded architecture or is attributed to the use of patches for dividing the inputs is unclear yet. This work introduces three patch-based frameworks for image registration using MLPs and transformers. We provide experiments on 2D-echocardiography registration to answer the former question partially and provide a benchmark solution. Our results on a large public 2D echocardiography dataset show that the patch-based MLP/Transformer model can be effectively used for unsupervised echocardiography registration. They demonstrate comparable and even better registration performance than a popular CNN registration model. In particular, patch-based models better preserve volume changes in terms of Jacobian determinants, thus generating robust registration fields with less unrealistic deformation. Our results demonstrate that patch-based learning methods, whether with attention or not, can perform high-performance unsupervised registration tasks with adequate time and space complexity. Our codes are available https://gitlab.inria.fr/epione/mlp\_transformer\_registratio