Using thermal inertia in district heating systems (DHSs) to improve the dispatch flexibility and economy of integrated heat and electricity systems (IHESs) is a research hotspot and difficulty. In most existing studies, the partial differential equations (PDEs) of thermal inertia are approximated by discrete-time models, making it difficult to accurately describe the continuous dynamic processes. In this paper, we propose a novel generalized phasor method (GPM) for thermal inertia in DHSs with constant mass flow. Based on the analytical solution of the PDEs and the Fourier transform, the intractable PDEs are transformed into a series of complex algebraic equations represented by phasors. The GPM has higher accuracy compared to traditional discrete models because it is essentially a continuous model in the time domain. Then, we present a different representation of an integrated heat and electricity dispatch (IHED) model combining a DHS model in phasor form and a traditional electrical power system model. The IHED model is a convex programming problem and can be easily solved. The effectiveness of the proposed GPM and dispatch model is verified in three test systems. Compared with traditional methods for modeling the thermal inertia, the proposed GPM is more accurate.

Pandemic brought new forms of remote education into the discussion. However, facilitating students' access to good computing infrastructure is not a widespread task. This paper presents a report on the use of computing resources in the AWS cloud by students in the Operating Systems class during the pandemic, from March to July 2020. The use of these computational resources was essential to consolidate some of the concepts covered in the course and, at the same time, to complement the students' knowledge about cloud computing. The results of this survey were very positive. Students said they learned more about the resources available in the cloud, the potential of cloud computing, and how to use it. Besides, they were able to create their remote infrastructure to carry out the work proposed in the Operating System class.

Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates simulation-based machine learning approaches from a device physics point of view. Strategies are suggested for achieving more realistic results.

How to represent spatiotemporal information in an artificial neuron model has been a problem of longstanding interest in artificial intelligence. After a brief review of recent advances, Caianiello's neuronic convolutional model is extended in this paper for spatiotemporal information representation. The kernel functions that correspond to the convolutional neuron's receptive field profile can be described by neural wavelets. The convolutional neuron-based multilayer network and its back propagation algorithm are developed to perform spatiotemporal pattern processing. The results provide a natural framework for the discussion of spatiotemporal information representation in an artificial neural network

Objective: Although spirometry is the most common pulmonary function test, there is no method to quantitatively infer about airway resistance or other properties from the flow-volume curves. Recently, an identifiable inverse model for forced expiration was proposed, as well as the idea to deduce changes in airway resistances and compliances from spirometric curve evolution. The aim of this work was to combine the above advances in a method for assessing the airway response to bronchial tests from a spirometric curve shift. Methods: The approach is based on the differential measurement of the degree, site of maximal effect and width of changes, further recalculated into relative changes in the distribution of airway resistances (δRg) and compliances (δCg) along the bronchial tree. To this end, appropriate models were identified using the pre- and post-test spirometry data. The accuracy was validated using sets of data simulated by the anatomy and physiology based models. Finally, the method was used to analyze the bronchodilation tests of three asthmatic subjects. Results: The expected errors in assessing the degree, site and width of changes in the zone of conducting airways were 6.3%, 2.4 generations and 22%, respectively, and for δRg and δCg were 5-10% and 13-16%, respectively. The analyses of clinical data indicated a significant reduction in resistances and an increase in compliances of airway generations 8-12, consistent with clinical knowledge. Conclusion: An unprecedented method to plausibly transforming the spirometry data into the site and degree of changes in airway properties has been proposed. Significance: The method can be used to deduce about the effects of bronchial tests, as well as to monitor changes in the airways between visits or to investigate how inhaled pharmaceuticals affect the bronchi.

With the increase of the number and geographic dispersion of distributed energy resources (DERs), the microgrid (MG) has become scattered along with the higher complexity of control and communication. This paper proposes a two-level regulation strategy to achieve the local objective of frequency regulation and power sharing as well as the global objective of economic operation in the real time for the scattered MG. Firstly, the MG integrated with multiple DERs is partitioned into several nanogrids (NGs) such that DERs connecting on the same feeder are grouped in the same NG for the efficient and cost-effective communication and control. Then in the NG level, the total power mismatch of the entire MG can be learned and an optimal incremental cost can be agreed by each NG through the fastest distributed linear averaging (FDLA) and discrete-consensus algorithm, respectively. In the DER level, a cost-driven droop gain is developed to indicate the willingness of each DER in the NG to participate into the frequency regulation service. The pinning-based protocol is formulated to regulate the frequency and meanwhile enable the proportional power sharing among DERs based on the economic droop function. Case studies satisfactorily demonstrate the effectiveness of the proposed regulation strategy for the economic frequency regulation in the tested MG.

Poynting theorem plays a very important role in analyzing electromagnetic phenomena. The electromagnetic power flux density is usually expressed with the Poynting vector. However, since Poynting theorem basically focuses on the power balance in a system, it is not so convenient in some situations to use it for evaluating the electromagnetic energies. The energy balance issue for time varying fields is revisited in this paper, and a set of energy balance equations are introduced, and a modified method for evaluating power flux is proposed.

[Note] Method in this article found as wrong and does not work. Entanglement is important on quantum gate based computer. Simple implementation of simulated entanglement on conventional digital circuits requires spatial complexity O(2^N) where N is number of qubits. In this article, array representation of entanglement which requires spatial complexity O(N^2) is proposed for simulated quantum gate on conventional digital circuits. Simulation shows proposed method can solve the problem which Deutsch-Jozsa algorithm solves.

Highlights of the article are: • Presented a systematic study of Deep Learning (DL), Deep Transfer Learning (DTL) and Edge Computing(EC) to mitigate COVID-19. • Surveyed on existing DL, DTL, EC, and Dataset to mitigate pandemics with potentialities and challenges. • Drawn a precedent pipeline model of DTL over EC for a future scope to mitigate any outbreaks. • Given brief analyses and challenges wherever relevant in perspective of COVID-19.

Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

This paper considers the deployment of intelligent reflecting surfaces (IRSs) technology for wireless multi-hop backhauling of multiple basestations (BSs) connected in a mesh topology. The performance of the proposed architecture is evaluated in terms of outage and symbol error probability in Rician fading channels, where closed-form expressions are derived and demonstrated to be accurate for several cases of interest. The analytical results corroborated by simulation, show that the IRS-mesh backhauling architecture has several desired features that can be exploited to overcome some of the backhauling challenges, particularly the severe attenuation at high frequencies.

This paper introduces the measure of approximate-degree and the concept of approximate-degree function between numerical values, thus developing a new interpolation method —— approximation-degree-based interpolation, i.e., AD interpolation. One-dimensional AD interpolation is done directly by using correlative interpolation formulas; n(n>1)-dimensional AD interpolation is firstly separated into n parallel one-dimensional AD interpolation computations to do respectively, and then got results are synthesized by Sum-Times-Difference formula into a value as the result value of the n-dimensional interpolation. If the parallel processing is used, the efficiency of n-dimensional AD interpolation is almost the same as that of the one-dimensional AD interpolation. Thus it starts a feasible and convenient approach and provides an effective method for high-dimensional interpolations. Furthermore, if AD interpolation is introduced into machine learning, a new instance-based learning method is expected to berealized.

Many studies were dedicated to the analysis of the propagation of High Energy Laser beams in free space. Laser beams propagation performance in the presence of atmospheric constituents may degrade by some interrelated linear and nonlinear phenomena. In this study, it is proposed a self-contained model including non-linear air temperature effect producing thermal distortion in laser propagation. In order to better understand the underlying phenomena, a set of quantifiers are described leading to the characterization of the laser thermal distortion. A parametric study is finally proposed in order to highlight the influence of the different parameters of the simulations.

We propose and develop comprehensive foundations for the analysis of nonlocal radiating systems using a special momentum-space approach. Part II focuses on building some basic examples and applications of the theory explicated in Part I.

Pretraining has become a standard technique in computer vision and natural language processing, which usually helps to improve performance substantially. Previously, the most dominant pretraining method is transfer learning (TL), which uses labeled data to learn a good representation network. Recently, a new pretraining approach – self-supervised learning (SSL) – has demonstrated promising results on a wide range of applications. SSL does not require annotated labels. It is purely conducted on input data by solving auxiliary tasks defined on the input data examples. The current reported results show that in certain applications, SSL outperforms TL and the other way around in other applications. There has not been a clear understanding on what properties of data and tasks render one approach outperforms the other. Without an informed guideline, ML researchers have to try both methods to find out which one is better empirically. It is usually time-consuming to do so. In this work, we aim to address this problem. We perform a comprehensive comparative study between SSL and TL regarding which one works better under different properties of data and tasks, including domain difference between source and target tasks, the amount of pretraining data, class imbalance in source data, and usage of target data for additional pretraining, etc. The insights distilled from our comparative studies can help ML researchers decide which method to use based on the properties of their applications.

We propose and develop comprehensive foundations for the analysis of nonlocal radiating systems using a special momentum-space approach. Part I focuses on the rigorous mathematical basis of the theory and their conceptual implications.

Postprint accepted on 21 May 2020 for publication in IEEE 92nd Vehicular Technology Conference. (c)2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

This paper introduces a systematic design method for decoupling elements, which can significantly improve the isolation between two co-located antennas, e.g. between transmit and receive antennas of an in-band full-duplex system. The design method applies the theory of characteristic modes for controlling the phase and amplitude of the scattered fields of the decoupling element, in order to optimally cancel the original incident fields which couple to the receiving antenna. We describe concisely the effects that characteristic angle, modal near-field, and modal excitation of the decoupling element have on the antenna isolation. For validating the proposed method, a planar wavetrap is designed and the isolation improvement verified with full-wave simulations. When we use the proposed method to optimize a wavetrap that is placed between two co-located patch antennas, we obtain an improvement of the isolation between the antennas by 33 dB at the centre frequency of their operational frequency band, and at least 12-dB improvement across the whole 142-MHz operational bandwidth of the two antennas. As a benchmark, the wavetrap is replaced by an absorber occupying 10 times the volume of the wavetrap. The absorber gives only 6 dB of isolation improvement, substantiating the effectiveness of the proposed wavetrap method.