Industrial loads are the major consumers of electricity in every utility. In every industry, the cost of electricity is the most significant expense besides raw materials cost, labor costs etc. But due to non-implementation of any incentive schemes for electricity consumption of the industries by the utilities, industries are also less interested in electricity demand management which in turn again increases their expenses incurred due to electricity. Without demand management of the consumer, the utilities also face difficulties in supplying the load and incurs more losses. In this paper, a comparative analysis has been made between the price of electricity consumption by an industry in normal tariff structure and in an incentive-based tariff scheme using fuzzy logic. After implementation of fuzzy based tariff, industrial loads support the utility by applying demand management and gets the highest benefits. The utilities also gain by reducing the losses during supply.

survey paper on graph neural networks based anomaly detection in social networks

This letter reports the design techniques for a class of inline waveguide bandpass filters with sharp-rejection capabilities at the lower stopband based on a novel nonlinear-frequency-variant-coupling (NFVC) structure. The proposed NFVC consists of a modified-T-shaped metallic post (MTP) that is placed at the center of the waveguide broad wall with its open arms lying along the waveguide width. The engineered NFVC structure produces a first-order bandpass filtering transfer function with a pair of transmission zeros (TZs) located below the passband range. To demonstrate the usefulness of the proposed NFVC inverter, a 9.9-GHz third-order inline waveguide bandpass filter prototype with two TZs is developed and tested. It consists of two half-wavelength cavity resonators coupled together via the conceived MTP coupling structure. The measured results are in close agreement with the EM simulated ones, thus validating the devised waveguide filter design principle.

Nowadays, System-on-Chip (SoC) components are found everywhere in all kinds of smart devices. Each System-on-Chip contains many different blocks that provide specific functionalities, such as WiFi or Bluetooth connectivity. Whereas integrating each such block in a SoC typically requires paying some royalties, not all blocks are necessary for all applications, or throughout a device’s lifecycle. Moreover, it is not possible to manufacture a specific SoC for each application. Significant advantages are therefore expected to be gained by enabling trustworthy remote SoC reconfiguration throughout their life cycles. A few approaches attempting to address this challenge have been proposed in the literature. They are typically based on Blockchain technology in order to support decentralization without relinquishing trust. Reviewing these approaches lead us to identify a potential flaw in the proposed protocols. Indeed, a SoC should be able to trust Blockchain information that it is given, without requiring any centralization. In order to validate our suspicions, we propose in this paper to use Verifpal: a cryptographic protocol verification tool that works from textual protocol models. We use it in a slightly unorthodox way in order to model the trust relationships in one of the approaches from the literature, and to verify it. The results show that, under some assumptions, a flaw is indeed present. We propose and model several possible fixes, and present their respective limitations.

Radio Frequency (RF) signals transmitted by Global Navigation Satellite Systems (GNSS) are exploited as signals of opportunity in many scientific activities, ranging from sensing waterways and humidity of the terrain to the monitoring of the ionosphere. The latter can be pursued by processing the GNSS signals through dedicated ground-based monitoring equipment, such as the GNSS Ionospheric Scintillation and Total Electron Content Monitoring (GISTM) receivers. Nonetheless, GNSS signals are susceptible to intentional or unintentional RF interferences (RFIs), which may alter the calculation of the scintillation indices, thus compromising the quality of the scientific data and the reliability of the derived space weather monitoring products. Upon the observation of anomalous scintillation indices computed by a GISTM receiver in the Mediterranean area, the study presents the results of the analysis and characterization of a deliberate, unclassified interferer acting on the L1/E1 GNSS signal bands, observed and captured through an experimental, software defined radio setup. The paper also highlights the adverse impacts of the interferer on the amplitude scintillation indices employed in scientific investigations, and presents a methodology to discriminate among regular and corrupted scintillation data. To support further investigations, a dataset of baseband signals samples affected by the RFI is available at IEEE DataPort.

*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. This article is intended to provide practical information about development of a cost-effective test model to achieve low-cost radar cross-section (RCS) measurement. To reduce the manufacturing cost of a model, two major possible approaches are: replacing the molding material with inexpensive one, and measuring a scaled-down model with a higher operating frequency. In this study, the former was achieved by using a resin-made model coated with conductive paste to imitate a complete metallic model. According to the scaling laws, the entire geometry, including the surface roughness of the model owing to the conductive paint, must be properly scaled down. As this roughness scaling is practically infeasible, we experimentally prove that the shape-only scaling is valid without the roughness scaling, provided that appropriate molding material is selected. As a preliminary study, two types of polyurethane resin with different density were tested for their suitability of the RCS measurement, and the resin with higher density was shown to be appropriate for the RCS test model. Based on this preliminary study, 1/20- and 1/40-scale aircraft models made with aluminum or polyurethane resin were manufactured, and their RCS were measured to demonstrate that the almost equivalent RCS patterns can be obtained; therefore, low-cost RCS testing was achieved by the resin-made scaled model.

It is commonly known that different brain waves are associated with different types of activities that we perform. This paper attempts to compare the activation states for Alpha, Beta, Delta, Gamma, Theta waves of three subjects, two of which are typical brains, and the third subject has Autistic Spectrum Disorder (ASD). The intent is to observe the brain patterns as the subjects perform conditions of normal and sports activity. We use Mind Monitor, MATLAB, EEGLAB to capture, analyze and plot the brain waves. We expected to observe differences in brain wave activation between typical and autistic brain. We also expected more activation in Alpha and Beta spectrums as those are associated more with the conditions we tested. The results were quite exciting as we saw differences in activation levels at the expected spectrum frequencies between the two types of brain. While this is more of a qualitative experiment than a quantitative one, we did observe a significant skew in Front Right Electrode activation for the autistic brain across all spectrums.

Under the implementation of the ‘Two Rules’, large-scale electric vehicles (EVs) with their V2G capability have become important incremental resources in the frequency regulation (FR) scenario. Based on the information interaction framework between the power grid and EVs in the form of the aggregator, the optimal decomposition of real-time automatic generation control (AGC) command from the power grid to EVs are discussed in this paper by proposing a bi-level decomposition algorithm considering the network model in combination with real-time dispatchable information of EVs. In the bi-level decomposition algorithm, on the one hand, the upper level decomposes the total AGC command between the aggregator and the AGC unit with economy as the goal and safety as the constraint. On the other hand, the AGC command is decomposed between each EV in the aggregator based on the real-time dispatchable power and the battery life loss under the condition of meeting the demand for EVs. The study case of the real-time AGC command decomposition shows that the proposed bi-level algorithm can realize the real-time command decomposition in the information interaction framework from the power gird to EVs in the perspective of economy and safety.

As the global industrial complex gears toward fulfilling the tenets of Industry 4.0 and beyond, technologies such as distributed ledger technologies, digital twins, and artificial intelligence become pivotal enablers. In the last decade, metaverse as a concept and technology found its place among crucial enablers for technology and digital advancement across several engineering domains. Metaverse has the potential to combine the elements from distributed computing platforms, the digital evolution of physical systems, and advanced learning systems to unearth a fully digitized world of comparative properties of the real world. We should ensure the privacy, integrity, and confidentiality of personal data. These requirements will lead to proper identity management in the metaverse. Given the complex nature of the metaverse, traditional centralized systems may not offer a viable identity management solution. Therefore, this study explores a decentralized identity management system called the Self-sovereign Identity (SSI) as a logical alternative to traditional centralized identity management systems. The proposed holistic framework aims to ignite new ideas and discussions related to the combined deployment of DLT, SSI, and metaverse to inspire new implementation areas within the Industry 4.0 environment. The paper also discusses various opportunities, enablers, technical \& privacy aspects, legislation requirements, and other barriers related to SSI implementation.

In this preprint we present a novel approach to solving the transport problem in semiconductors.  We reformulate the drift-diffusion equations in terms of the quasi-Fermi-energies as solution variables; a drastic increase in numerical stability is achieved, which permits the simulation of devices at cryogenic temperatures as well as wide-band-gap devices using double precision arithmetic, instead of extended precision arithmetic which would otherwise be required to solve these applications using regular drift-diffusion.

Most studies on learning from noisy labels rely on unrealistic models of i.i.d. label noise, such as class-conditional transition matrices. More recent work on instance-dependent noise models are more realistic; however, these models assume a single generative process for label noise across the entire dataset, which does not accurately capture the real-world labeling dynamics observed in modern dataset collection. We propose a new model of label noise, which we call “labeler-dependent noise (LDN).” LDN extends and generalizes instance-dependent noise to multiple labelers, based on the observation that modern datasets are typically constructed via distributed data gathering and labeling methods. Furthermore, inspired by recent research in social learning theory, we introduce a modular learning framework under our proposed labeler-dependent noise model called “labeler-aware learning”, in which each labeler is estimated and compared against the others during the learning process. Our experimental results demonstrate how previous state-of-the-art approaches for learning from noisy labels are unable to handle the general model of label noise, while our labeler-aware learning framework remains robust even when large fractions of labelers are spammers.

Modern distribution networks are undergoing several technical challenges, such as voltage fluctuations, because of high penetration of distributed energy resources (DERs). This paper proposes a deep reinforcement learning (DRL)-based Volt- VAR co-optimization technique for reducing voltage fluctuations as well as power loss under high penetration of DERs. In addition, the proposed approach minimizes the operational cost of the grid. A stochastic policy optimization based soft actor critic (SAC) agent is proposed to configure the optimal set-points of the reactive power outputs of the inverters. The performance of the proposed model is verified on the modified IEEE 34- and 123-bus systems and compared with a base case scenario with no reactive supply by inverters, and a local droop control approach. The results demonstrate that the proposed framework outperforms the conventional droop control method in improving the voltage profile, minimizing the network power loss, and reducing grid operational cost.

In this work, a proof of concept for 5G-compliant user-equipment side sensing is presented. It is based on orthogonal frequency division multiplexing radar-based ranging which is realized in this work by using the sounding reference signal from the 5G New Radio standard. The signal configuration and thus the corresponding waveform is generated in compliance with the existing 3rd Generation Partnership Project standard for 5G. It is an uplink physical signal and is originally intended, amongst others, for channel estimation. The used model is introduced, followed by the sounding reference signal. This leads to a first proof of concept by presenting simulation and measurement results. We show, that a range estimation error in the order of centimeters is achievable.

Comparators are utilised by Nyquist-rate and oversampling analog to digital converters (ADCs) to accomplish quantization and perhaps sampling. Thus, comparators have a  substantial effect on the speed and accuracy of ADCs. This study provides a revised design for a dynamic-latch-based comparator that achieves the lowest latency, maximum area-efficient realisation, reduced power dissipation, and low offset. The proposed circuit has been designed and simulated using GDPK  45 nm standard CMOS-Process to operate on 100 MHz clock, at  1.2V supply voltage. Design and simulation have been carried  out using CADENCE Virtuoso EDA tool. Compared to the  original design, the PDP was easily reduced by approximately  by 6% with offset voltage reduced by 8 mV without speed trade off.

Reliability option (RO) is a generation capacity adequacy mechanism based on option concept in financial products. Recent studies on RO lack the consideration of the game between power producers. In this paper, the market equilibrium under RO mechanism is studied and a bilevel model is proposed. The upper-level models are maximizing the profits of power producers and the lower-level model is market clearing. Electricity sales income, option fee income and expenditure including fuel cost, expansion investment cost, refund cost and penalty are considered to evaluate each power producer’s profit. The bilevel model is reformulated into the mathematical problem with equilibrium constraints (MPEC) and a series of linearization methods are used. The market equilibrium is obtained by diagonalization iteration for all MPECs. Then, some cases are set to analyze the capacity investment and the comprehensive purchasing cost of users in the equilibrium state. Finally, suggestions on the construction of RO mechanism are given.

The growing penetration of renewable energy resources such as wind and solar into the electric power grid has changed grid dynamics. Particularly, the large-scale displacement of synchronous generators with inverter-based resources significantly reduces the short-circuit strength and thus increases the risk of the small-disturbance stability issues, such as sub/super-synchronous oscillation issues, resulting from fast-acting inverter controls and the power network. It is challenging to assess grid strength in terms of small-disturbance stability in power systems in high penetration level of inverter-based resources due to the complex interaction between power networks and different inverter control configurations such as grid-forming and grid-following inverters. It becomes more challenging for the 100% inverter-based power system, where all synchronous generators are displaced with inverter-based apparatus. In such a power system, the commonly used methods for grid strength assessment are invalid due to their assumption that synchronous generators are used to provide voltage support for grid strength assessment. To address the challenges, this paper presents a method for assessing grid strength in terms of small-disturbance stability in a 100% inverter-based power system. First, we reveal voltage support characteristics of grid-forming inverters. Then, we further analyze the small-disturbance stability of a multi-inverter system of grid-forming and grid-following inverters. On this foundation, a method is proposed by extending our previously developed generalized short-circuit ratio to assess grid strength in terms of the small-disturbance stability of the 100% inverter-based power system while considering the complex interaction between grid-forming and grid-following inverters coupled by the power network. The efficacy of the proposed method is demonstrated on a modified two-area-four-machine system and a modified IEEE 39-bus system via the eigenvalue analysis and electromagnetic transient simulation.

Due to physical and monetary constraints, quality-related variables in the process industries are generally difficult to measure using hardware sensors. Therefore, they are often not available as frequently as the other variables since obtaining them typically via laboratory analysis are time-consuming. On the other hand, the dynamic nature of easy-to-measure process data contains valuable predictive information about the quality variables. Consequently, modelling sequential data is beneficial in building a soft sensor that can predict the quality variable. Several techniques were proposed to extract dynamic features as measured data often suffers from noise and collinearity. This paper presents a semi-supervised oscillating slow feature inference network to address certain shortcomings of the existing solutions. The proposed learning algorithm uses a gated recurrent unit to learn a combined inference network for missing quality variable imputation and oscillating slow feature extraction from nonlinear process data. We evaluate the efficacy of the proposed methodology on a simulated and an industrial process.

Study estimated the number of real-world, unsupervised steps that need to be recorded before various gait-related outcomes show consistency (i.e., adding additional data would not change the outcome distribution). The data were collected over 1-week of walking by healthy adults and adults with symptomatic knee osteoarthritis using a shoe-embedded inertial sensor.