This paper includes an approach to summarize energy systems of countries worldwide in energy system archetypes. These archetypes are generated by a clustering algorithm and modeled in an energy system optimization model by standardized rules. The results show that archetypes provide an opportunity to compare decarbonization pathways of different countries on a global level. Furthermore, they can be used to simplify data collection and elaborate the potential of new technologies in different energy systems.

This work proposes a novel concept for a semi-autonomous human-cyber-physical system (HCPS) to operate next-generation wind turbines on the way towards Industry 5.0. The exponential increase in the complexity of future wind turbines requires artificial intelligence (AI) to operate the machines efficiently and consistently. Evolving current Industry 4.0 digital twin technology beyond a sole aid for a human decision-making process, the digital twin in the proposed system is utilized for highly effective training of the AI through machine learning. Human intelligence (HI) is elevated to a supervisory level, making high-level decisions through a human-machine interface to break the autonomy when needed. This paper points out a plausible way to realize the HCPS by identifying strategic development demands for the key enabling technologies projected from readily available knowledge.

This paper introduces a non-parametric learning framework to combat outliers in online, multi-output, and nonlinear regression tasks. A hierarchical-optimization problem underpins the learning task: Search in a reproducing kernel Hilbert space (RKHS) for a function that minimizes a sample average $\ell_p$-norm ($1 \leq p \leq 2$) error loss on data contaminated by noise and outliers, subject to side information that takes the form of affine constraints defined as the set of minimizers of a quadratic loss on a finite number of faithful data devoid of noise and outliers. To surmount the computational obstacles inflicted by the choice of loss and the potentially infinite dimensional RKHS, approximations of the $\ell_p$-norm loss, as well as a novel twist of the criterion of approximate linear dependency are devised to keep the computational-complexity footprint of the proposed algorithm bounded over time. Numerical tests on datasets showcase the robust behavior of the advocated framework against different types of outliers, under a low computational load, while satisfying at the same time the affine constraints, in contrast to the state-of-the-art methods which are constraint agnostic. ——- © 20XX 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.

A brief overview of present electricity demand, supply, projection of them and renewable energy generation and progress has been presented in this paper. Electricity is the major source of power for most of the country’s economic activities. According to the Bangladesh Power Development Board in July 2018, 90 percent of the population had access to electricity. However per capita energy consumption in Bangladesh is considered low. The country has a very limited energy reserve; small amounts of oil, coal and countable natural gas reserves. The country suffers an internal energy struggle, as about 88% of the country’s power producing thermal plants are gas-based. To solve this problem, renewable energy is being used.

Abstract: In system identification scenarios, classical adaptive filters, such as the recursive least squares (RLS) algorithm, predict the system impulse response. If a tracking delay is acceptable, interpolating estimators capable of providing more accurate estimates of time-varying impulse responses can be used; channel estimation in communications is an example of such applications. The basis expansion model (BEM) approach is known to be efficient for non-adaptive (block) channel estimation in communications. In this paper, we combine the BEM approach with the sliding-window RLS (SRLS) algorithm and propose a new family of adaptive filters. Specifically, we use the Legendre polynomials, thus the name the SRLS-L adaptive filter. The identification performance of the SRLS-L algorithm is evaluated analytically and via simulation. The analysis shows significant improvement in the estimation accuracy compared to the SRLS algorithm and a good match between the theoretical and simulation results. The performance is further investigated in application to the self-interference cancellation in full-duplex underwater acoustic communications, where a high estimation accuracy is required. A field experiment conducted in a lake shows significant improvement in the cancellation performance compared to the classical SRLS algorithm.

We start with a detailed review of the PACT approach for model order reduction of RC networks. We then develop a method that uses PACT as a preprocessing step to transform a generic lumped RC transmission line of some nominal order, based on a nominal (r,c) setting, into a parameterized circuit captured in a SPICE sub-circuit description. Then, given any other lumped RC line of the same order, we pass its (r,c) setting as parameters to this sub-circuit so as to automatically transform and reduce the line into a reduced order model without having to rerun PACT. In this way, we effectively characterize lumped RC transmission lines in a way that allows them to be reduced on-the-fly without any expensive processing.

This paper investigates the impact on the optical signal-to-noise ratio (OSNR) of the residual per span (RDPS) in a N × 100km dispersion managed system with zero total accumulated dispersion from input to output using split step Fourier method (SSFM) -Monte Carlo simulation. This paper shows that the nonlinear interference NLI does in-fact impact the performance yielding different best working power depending on the value of Nx100 km span and the type of dispersion managed link. The paper shows that dispersion uncompensated optical links are preferable to dispersion managed fibers in equalizing NLI effects in long haul optical links.

This paper presents a generic algebraic proof of a recently published theorem [4], on the power conservative equivalent circuit for linear DC networks formed by time-invariant resistors and independent voltage and current sources. As the cited publication states, the internal losses of any network have two components: one variable and dependent on the internal resistances of the actual circuit and the power transferred to the pair of accessible terminals; and the other constant and dependent only on the internal voltage and current sources and the resistances of the actual network. It is also noted that the traditional Thévenin and Norton equivalent circuits are particular cases of the proposed equivalent circuit.

In this paper, we propose a unified probabilistic decentralised control and message passing framework for real time control of the electrical grid which enables the development of the future smart grid. The key elements of the proposed framework are the design of local randomised controllers and probabilistic message passing methodology which enables the coordination between the designed local controllers to account for optimisation considerations on the system operation. Within the proposed framework, the electric grid is decomposed into a number of control areas. Additionally, since the frequency is the ubiquitous grid state variable, representing the balancing of active power generation and consumption, the proposed framework is demonstrated based on local Load Frequency Control (LFC). Simulation studies involving a six-area power system and three interconnection schemes are carried out to illustrate the applicability and effectiveness of the proposed approach.

We review recent work on broadband RF channelizers based on integrated optical frequency Kerr micro-combs combined with passive micro-ring resonator filters, with microcombs having channel spacings of 200GHz and 49GHz. This approach to realizing RF channelizers offers reduced complexity, size, and potential cost for a wide range of applications to microwave signal detection.

The wireless communication is the fastest-growing field of studies to enable humans to communicate with each other. Introducing a new blockchain architecture with big data analytics can be an enticing platform towards increasing connectivity performance throughout the smart cities. Blockchains can build a convenient mechanism allowing two devices to effectively transmit currency as well as data, to each other with a secure and reliable agreement. Smart cities provide an opportunity to connect people and places that use emerging technologies that help to improve urban planning and development. Smart cities can enhance public infrastructure and people's living experience. Presently, high-speed, intelligent, effective with several innovations, like low energy consumption, etc., seem to be accessible to interact together in the modern environment. By using the blockchain-based big data approach the physical devices are allowed to communicate securely with other physical devices in heterogeneous environments. This approach builds a new blockchain-based computing structure throughout the IoT technology configuration. This approach can examine blockchain technology to the underlying technology or maintains the IoT authentication reliable. This strengthens blockchain and cloud to develop an enabling IoT pervasive environment for secure communication among the physical objects.

Noninteger dimensionality, which shows up in many fields of physics and engineering, is generally viewed from the perspective of fractals and measured by the Hausdorff dimension. Motivated by information theoretic considerations and by extending the principle of complementarity, we propose a new interpretation in which it engenders a potential. The inverse square law may then be seen to originate from the properties associated with the noninteger dimensional space, and it further indicates a relationship between the dynamics and the actual value of the dimensions that may be used for experimental test of the interpretation.

Modern training typically incorporates real-world training applications. Augmented Reality (AR) technologies support this by overlaying virtual objects in real-world 3-Dimensional (3D) space. However, integrating instruction into AR is challenging because of technological and educational considerations. One reason is a lack of architecture for supporting Intelligent Tutoring Systems (ITSs) in AR training domains. We present a novel modular agent-based Distributed Augmented Reality Training (DART) architecture for ITSs to address two key AR challenges: (1) a decoupling of the display and tracking components and (2) support for modularity. Modular agents communicate with each other over a network, allowing them to be easily swapped out and replaced to support differing needs. Our motivation is driven by the fact that AR technologies are vary considerably and an ITS architecture would need to be flexible enough to support these requirements. Finally, we believe that our novel architecture will appeal to practical designers of ITSs and to the more theoretical educators who wish to use such systems to simulate and broaden research in the distributed cognitive educational theories.

Satellite image time series (SITS) classification is a major research topic in remote sensing and is relevant for a wide range of applications. Deep learning approaches have been commonly employed for SITS classification and have provided state-of-the-art performance. However, deep learning methods suffer from overfitting when labeled data is scarce. To address this problem, we propose a novel self-supervised pre-training scheme to initialize a Transformer-based network by utilizing large-scale unlabeled data. In detail, the model is asked to predict randomly contaminated observations given an entire time series of a pixel. The main idea of our proposal is to leverage the inherent temporal structure of satellite time series to learn general-purpose spectral-temporal representations related to land cover semantics. Once pre-training is completed, the pre-trained network can be further adapted to various SITS classification tasks by fine-tuning all the model parameters on small-scale task-related labeled data. In this way, the general knowledge and representations about SITS can be transferred to a label-scarce task, thereby improving the generalization performance of the model as well as reducing the risk of overfitting. Comprehensive experiments have been carried out on three benchmark datasets over large study areas. Experimental results demonstrate the effectiveness of the proposed method, leading to a classification accuracy increment up to 2.38% to 5.27%. The code and the pre-trained model will be available at https://github.com/linlei1214/SITS-BERT upon publication. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

In this paper, we investigate the transmission probabilities in three cases (depending only on the legitimate receiver, depending only the eavesdropper, and depending on both legitimate receiver and eavesdropper) in quantum key distribution (QKD) systems over free-space optical links. To be more realistic, we consider a generalized pointing error scenario, where the azimuth and elevation pointing error angles caused by stochastic jitters and vibrations in the legitimate receiver platform are independently distributed according to a non-identical normal distribution. Taking these assumptions into account, we derive approximate expressions of transmission probabilities by using the Gaussian quadrature method. To simplify the expressions and get some physical insights, some asymptotic analysis on the transmission probabilities is presented based on asymptotic expression for the generalized Marcum Q-function when the telescope gain at the legitimate receiver approaches to infinity. Moreover, from the asymptotic expression for the generalized Marcum Q-function, the asymptotic outage probability over Beckmann fading channels (a general channel model including Rayleigh, Rice, and Hoyt fading channels) can be also easily derived when the average signal-to-noise ratio is sufficiently large, which shows the diversity order and array gain.

Increasing share of renewables poses new challenges towards planning and operation of power systems all over the world, thereby, requiring new methods and tools. This article explains the value of balancing tool chain which encompasses operation of market and power system from day-ahead planning until the real-time dynamic simulation. The salient features for such a balancing tool chain is discussed in this article. A Balancing Tool Chain has been developed and validated based on balancing principles of Denmark and Europe. The efficacy of the balancing tool chain is demonstrated through very detailed case studies of massive penetration of offshore wind power in North Sea countries towards 2050. Using detailed case studies, recommendations are provided towards balancing of North Sea countries in terms of operational and balancing practices as well as dimensioning of reserves for future scenarios.

Video game consoles share many of the characteristics of an ideal device for use in enterprise deployments. In comparison to many desktop and notebook PCs available in the market, modern video game consoles are actually quite powerful and capable. They provide an excellent user experience with simple and intuitive setup and operation. At the heart of the design of many modern video game consoles is security; they are remarkably resilient against very sophisticated hardware and software attacks. They are also rather cost-effective in comparison to modern PCs.

Concerns for spectrum congestion have spurred extensive research efforts on efficient spectrum management. Therefore, devising schemes for spectrum sharing between radar and wireless communication systems has become an important area of research. Joint communications-radar (JCR) system is among the several approaches proposed to achieve this objective. In JCR systems, additional components and processes are added to an existing standardized communication platform to enable radar functions. Moreover, the communication waveform is used as an integrated JCR waveform, i.e., the same signal is used to communicate information to a receiver and to perform radar detection and estimation operations for a nearby target. The most common application of JCR systems is found in vehicle-to-vehicle (V2V) communication scenario. In this article, an overview of the spectrum sharing methods is presented, with focus on JCR systems in automotive applications. We first review the recent works on IEEE 802.11p- and IEEE 802.11ad-based radars. A basic description of the modeling of JCR system and channels is presented, followed by discussions on main components and processes employed in various JCR systems. Mainly, we are interested in how the radar detection and estimation functions are performed in conjunction with the communication receiver functions with minimal alterations in the existing system. At the end of the paper, some performance trade-offs between the communication and radar sub-systems are also discussed.