Advanced Schemes Research Articles

Design and Implementation of a SiC-Based Multifunctional Back-to-Back Three-Phase Inverter for Advanced Microgrid Operation.

Because of the worldwide trend of microgrid (MG) and renewable energy (RE)-based distributed power generation (DG), advanced power flow control schemes with wide bandgap (WBG) semiconductor technologies to ensure high-level performance of grid-connected MGs is one of the crucial research topics. In grid-connected MGs, a static switch (SS) is commonly used at the point of common coupling (PCC) of two systems. In this paper, the role of SS is replaced by a SiC-based three-phase back-to-back (BTB) inverter system for seamless switching between grid-connected and standalone modes through advanced power flow control schemes. According to scenarios of different grid/load conditions and available DG capacities in an MG, various advanced control functions can be developed for both MG operating modes: bidirectional control of active and reactive power flows, seamless switching between operating modes, improvement of grid power quality (PQ), and voltage stabilization. In this paper, mathematical models of the BTB inverter in a synchronous reference frame (SRF) is first derived, and the required controllers are then designed. For functional testing, two typical cases are simulated and analyzed in a MATLAB/Simulink environment and then verified through 1kVA small-scale hardware implementation with Texas Instruments (TI) digital signal processor (DSP) TMS320LF2812 as the control core. Results show satisfactory performances of power flow control and PQ improvement of MG.

Modulation and coding methods in 5th generation networks

The fifth generation (5G) of wireless communication networks represents a groundbreaking shift in the realm of connectivity, enabling a wide array of transformative applications and services. This article delves into the pivotal role played by modulation and coding methods in the context of 5G networks. It explores their significance, delves into recent research and publications within the field, and outlines the direction for future research endeavors. Modulation and coding (M&C) are fundamental for 5G networks, enhancing data rates, reliability, and reducing interference. This article delves into their role in 5G networks, addressing challenges, including fading channels, interference, and diverse user needs. Recent research explores advanced modulation schemes like 256-QAM and 1024-QAM, requiring error correction coding techniques like Turbo codes and LDPC codes. Massive MIMO, using multiple antennas at both ends, is supported by adaptive coding and modulation. Adaptive Modulation and Coding (AMC) dynamically adjusts schemes based on real-time channel conditions, balancing data rate and reliability. Non-Orthogonal Multiple Access (NOMA) accommodates massive device connectivity, allowing multiple users to share resources with different power levels and modulation schemes. The study aims to comprehensively analyze 5G's M&C methods, assess modulation schemes for higher data rates, evaluate error correction coding, investigate Massive MIMO and adaptive modulation for spectral efficiency, and scrutinize NOMA's potential for massive device connectivity. Results highlight the importance of advanced modulation schemes, efficient error correction coding, and adaptive techniques in realizing 5G objectives, enabling higher data rates, lower latency, and improved reliability. NOMA is promising for accommodating many connected devices in 5G. M&C are essential for efficient and reliable data transmission in 5G, with ongoing research refining techniques for 6G and beyond. Integrating machine learning and AI into M&C algorithms holds promise for enhancing network efficiency and reliability.

The extreme heat wave of July-August 2021 in the Athens urban area (Greece): Atmospheric and human-biometeorological analysis exploiting ultra-high resolution numerical modeling and the local climate zone framework.

Greece was affected by a prolonged and extreme heat wave (HW) event (July 28-August 05) during the abnormally hot summer of 2021, with the maximum temperature in Athens, the capital of the country, reaching up to 43.9 °C in the city center. This observation corresponds to the second highest maximum temperature recorded since 1900, based on the historical temperature time series of the National Observatory of Athens weather station at Thissio. In the present study, a multi-scale numerical modeling system is used to analyze the urban climate and thermal bioclimate in the Athens urban area (AUA) in the course of the HW event, as well as during 3 days prior to the heat wave and 3 days after the episode. The system consists of the Weather Research and Forecasting model, the advanced urban scheme BEP/BEM (Building Energy Parameterization/Building Energy Model) and the human-biometeorological model RayMan Pro, and incorporates the local climate zone (LCZ) classification scheme. The system's validation results demonstrated a robust modeling set-up, characterized by high capability in capturing the observed magnitude and diurnal variation of the urban meteorological and heat stress conditions. The analysis of two- and three-dimensional fields of near-surface air temperature, humidity and wind unraveled the interplay of geographical factors (surface relief and proximity to the sea), background atmospheric circulations (Etesians and sea breeze) and HW-related synoptic forcing with the AUA's urban form. These interactions had a significant impact on the LCZs heat stress responsiveness, expressed using the modified physiologically equivalent temperature (mPET), between different regions of the study area, as well as at inter- and intra-LCZ level (statistically significant differences at 95 % confidence interval), providing thus, urban design and health-related implications that can be exploited in human thermal discomfort mitigation strategies in AUA.

Auto-Tuning with Reinforcement Learning for Permissioned Blockchain Systems

In a permissioned blockchain, performance dictates its development, which is substantially influenced by its parameters. However, research on auto-tuning for better performance has somewhat stagnated because of the difficulty posed by distributed parameters; thus, it is possible only with difficulty to propose an effective auto-tuning optimization scheme. To alleviate this issue, we lay a solid basis for our research by first exploring the relationship between parameters and performance in Hyperledger Fabric, a permissioned blockchain, and we propose Athena, a Fabric-based auto-tuning system that can automatically provide parameter configurations for optimal performance. The key of Athena is designing a new Permissioned Blockchain Multi-Agent Deep Deterministic Policy Gradient (PB-MADDPG) to realize heterogeneous parameter-tuning optimization of different types of nodes in Fabric. Moreover, we select parameters with the most significant impact on accelerating recommendation. In its application to Fabric, a typical permissioned blockchain system, with 12 peers and 7 orderers, Athena achieves a throughput improvement of 470.45% and a latency reduction of 75.66% over the default configuration. Compared with the most advanced tuning schemes (CDBTune, Qtune, and ResTune), our method is competitive in terms of throughput and latency.

Nonlinear Model Predictive Control with L1 Cost-Function Using Neural Networks for Multivariable Processes*

This work presents a computationally Efficient Model Predictive Control (MPC) algorithm for nonlinear Multiple Input Multiple Output (MIMO) processes in which the sum of absolute values of predicted control errors (the L1 norm) is minimized rather than the typically used sum of squared errors (the L2 norm). An approximator of the absolute value function combined with an advanced online trajectory linearization scheme is used to obtain a computationally uncomplicated algorithm that requires solving online quadratic optimization tasks. For a nonlinear MIMO neutralization process, we show that the described algorithm gives control quality comparable to that possible in MPC with nonlinear optimization. We examine the effectiveness of polynomial and neural approximation of the absolute value function. Moreover, we show that the described algorithm gives better control quality than the classical approach to MPC with the L2 cost-function.

A polar-switchable and controllable negative phototransistor for information encryption.

Anomalous negative phototransistors have emerged as a distinct research area, characterized by a decrease in channel current under light illumination. Recently, their potential applications have been expanded beyond photodetection. Despite the considerable attention given to negative phototransistors, negative photoconductance (NPC) in particular remains relatively unexplored, with limited research advancements as compared to well-established positive phototransistors. In this study, we designed ferroelectric field-effect transistors (FeFETs) based on the WSe2/CIPS van der Waals (vdW) vertical heterostructures with a buried-gated architecture. The transistor exhibits NPC and positive photoconductance (PPC), demonstrating the significant role of ferroelectric polarization in the distinctive photoresponse. The observed inverse photoconductance can be attributed to the dynamic switching of ferroelectric polarization and interfacial charge transfer processes, which have been investigated experimentally and theoretically using Density Functional Theory (DFT). The unique phenomena enable the coexistence of controllable and polarity-switchable PPC and NPC. The novel feature holds tremendous potential for applications in optical encryption, where the specific gate voltages and light can serve as universal keys to achieve modulation of conductivity. The ability to manipulate conductivity in response to optical stimuli opens up new avenues for developing secure communication systems and data storage technologies. Harnessing this feature enables the design of advanced encryption schemes that rely on the unique properties of our material system. The study not only advances the development of NPC but also paves the way for more robust and efficient methods of optical encryption, ensuring the confidentiality and integrity of critical information in various domains, including data transmission, and information security.

Limited Feedback Design Based on Kronecker Product Codebook for Massive MIMO Systems

To realize massive multiple-input multiple-output (MIMO) systems, the uniform planar array (UPA) structure has been adopted to deploy a large number of antennas in a limited area of a radio unit at a base station. Subsequently, the Kronecker product (KP) codebook, consisting of horizontal and vertical-domain codebooks, was introduced to enable efficient quantization and feedback for the channel state information (CSI) under the UPA structure. In this paper, we propose advanced limited feedback schemes based on the KP codebook for massive MIMO systems. First, we propose one feedback bit allocation scheme to maximize the beamforming gain. Eigenvalues of spatial correlation channels are used to determine the numbers of feedback bits for horizontal and vertical-domain CSI, and the numbers of feedback bits to maximize the beamforming gain are derived as a closed-form expression. Moreover, we propose a novel feedback and pilot transmission scheme to reduce both the feedback and pilot overhead. The proposed feedback and pilot transmission scheme utilizes the property that the small-scale fading information can be compressed only to the horizontal-domain CSI when the spatial correlation of the vertical-domain channel is high. Simulation results show that, with the use of the proposed feedback bit allocation, the beamforming gain increases by up to 65% compared with the fixed feedback bit allocation when the horizontal and vertical-domain spatial correlation coefficients are equal to 0.57, and the numbers of horizontal and vertical-domain antennas are 8 and 12, respectively. In addition, it is also shown that the proposed feedback and pilot transmission scheme improves the ergodic rate by up to 48% compared with conventional feedback schemes.

Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing: Nonlocal sparse and low-rank modeling

The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employ sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structure of image patches by optimizing their sparse representations or learning deep neural networks while preserving the known or modeled sensing process. Beyond exploiting local image properties, advanced CS schemes adopt nonlocal image modeling by extracting similar or highly correlated patches at different locations of an image to form a group to process jointly. More recent learning-based CS schemes apply nonlocal structured sparsity priors using group sparse (and related) representation (GSR) and/or low-rank (LR) modeling, which have demonstrated promising performance in various computational imaging and image processing applications.

Sensor fault-tolerant control for wind turbines: an iterative learning method

The combined wind speed estimator and tip speed ratio (WSE-TSR) tracking control scheme is widely used to regulate power production for large-scale modern wind turbines. Although very effective, such an advanced control scheme, based on the prior model information, is highly dependent on external measurements. For partial-load region control, the only external information involved is commonly the measured rotor or generator speed. Inaccuracy in such sole measurement results in an unintended turbine operation and might lead to sub-optimal power production and instability. This paper presents a fault-tolerant control (FTC) method, which aims to eliminate the sensor fault effects for modern wind turbine systems. To fulfil this goal, an iterative learning scheme is proposed to detect and estimate the multiplicative sensor fault, on which an adaptive FTC law is formulated such that the effects of the sensor fault are eliminated. Case studies show that the proposed iterative learning FTC method performs well in detecting, estimating, and accommodating the sensor fault under realistic turbulent wind conditions. The advanced wind turbine controller can maintain its control performance even under faulty conditions, preventing further damage to other turbine components and allowing for continuous power production.

Flower pollination global peak search algorithm for partially shaded solar photovoltaic system

Photovoltaic (PV) systems are required to function at their maximum power point (MPP) to fully utilize solar energy. Solar PV panels are configured with maximum power point tracking (MPPT) systems to enhance the generation and supply of the maximum available power. However, owing to external environmental factors and partial shading conditions (PSCs), the tracking process becomes complex. Conventional MPPT methods cannot always achieve the global MPP. Thus, an advanced metaheuristic MPPT scheme for PV systems, the flower pollination algorithm (FPA), is implemented in this study to find the best global duty cycle to obtain the maximum power output, and a Levy flight is utilized to enhance FPA convergence. The FPA MPPT model was established, and its performance was compared with the conventional, and some meta-heuristic-based MPPT techniques; perturb and observe (PO), incremental conductance (INC), particle swarm optimization (PSO), cuckoo search (CS), grey wolf optimization (GWO), genetic algorithm (GA), double integral sliding mode control (DISMC), and hill-climbing (HC). Different scenarios were considered for the simulation: partial shading, complex partial shading, long-width, and long-length partial shading, and different temperature conditions employing series and series-parallel PV array configurations. In addition, real-world field atmospheric irradiance data for Daejeon city is used for the study. In terms of efficiency, the FPA attains at most 83%–96% of the expected GMP from the PV array as compared with the other MPPT techniques in all scenarios considered. The adopted FPA obtained the following root mean square error (RMSE), mean relative error (MRE), and mean absolute error (MAE) values respectively for each Scenario as follows: Scenario I: RMSE: 0.0061, MRE: 0.0463, MAE: 6.0745e-06, Scenario II: RMSE: 0.0116, MRE: 0.0430, MAE: 1.0738e-05, Scenario III: RMSE: 0.0640, MRE: 0.206, MAE: 4.8239e-05, Scenario IV (varying temperature conditions): RMSE: 0.0062–0.0074, MRE: 0.0460–0.0548, MAE: 6.1333e-06 – 7.4922e-06. A unique sensitivity performance comparison analysis is done employing the power spectral analysis to validate the effectiveness of the adopted FPA to the other MPPT techniques. The FPA performs better than all the techniques in all scenarios in comparing their maximum power tracked, convergence speed and accuracy, system efficiency, and optimum statistical results attained.

A survey of multi-agent Human–Robot Interaction systems

This article presents a survey of literature in the area of Human–Robot Interaction (HRI), specifically on systems containing more than two agents (i.e., having multiple humans and/or multiple robots). We identify three core aspects of “Multi-agent” HRI systems that are useful for understanding how these systems differ from dyadic systems and from one another. These are the Team structure, Interaction style among agents, and the system’s Computational characteristics. Under these core aspects, we present five attributes of HRI systems, namely Team size, Team composition, Interaction model, Communication modalities, and Robot control. These attributes are used to characterize and distinguish one system from another. We populate resulting categories with examples from the recent literature along with a brief discussion of their applications. We also analyze how these attributes in multi-agent systems differ from the case of dyadic human–robot systems. Through this survey, we summarize key observations from the current literature, and identify challenges and promising areas for future research in this domain. In order to realize the vision of robots being part of the society and interacting seamlessly with humans, there is a need to expand research on multi-human–multi-robot systems. Not only do these systems require coordination among several agents, they also involve multi-agent and indirect interactions which are absent from dyadic HRI systems. Including multiple agents in HRI systems requires more advanced interaction schemes, behavior understanding and control methods to allow natural interactions among humans and robots. In addition, research on human behavioral understanding in mixed human–robot teams also requires more attention. This will help formulate and implement effective robot control policies in HRI systems with large numbers of heterogeneous robots and humans; a team composition reflecting many real-world scenarios.

4 × 25 Gb/s PAM4 optical transmitter for micro‐ring modulator with thermal control

AbstractThe explosive growth of bandwidth demands of data center and super‐computing systems spur the appliance and development of optoelectronic‐based solutions such as micro‐ring modulator (MRM) transmitters, and advanced modulation schemes such as four‐level pulse‐amplitude modulation (PAM4). In this paper, a 4 × 25 Gb/s PAM4 MRM transmitter fabricated in 0.13 μm SiGe BiCMOS technology is presented. It consists of four‐channel MRM driver and thermal control loop. Capacitor and resistor degeneration techniques and inductor peaking techniques are adopted to expand channel bandwidth. Meanwhile, the predistortion technique is utilized to compensate for MRM nonlinearity, and auto gain control (AGC) circuits to enable the MRM driver to realize a fixed differential output swing of 3.5 Vpp. Furthermore, a thermal control method based on analog to digital converter, digital to analog converter, and logic control circuits is proposed to compensate for the temperature sensitivity of MRM. The measurement results show that the 25 Gb/s nonreturn‐to‐zero optical eye achieves an extinction ratio of 5.63 dB, and the PAM4 eye can be observed up to 40 Gb/s.

Protection Schemes in HPON Networks Based on the PWFBA Algorithm.

In this paper, possibilities for network traffic protection in future hybrid passive optical networks are presented, and reasons for realizing and utilizing advanced network traffic protection schemes for various network traffic classes in these networks are analyzed. Next, principles of the Prediction-based Fair Wavelength and Bandwidth Allocation (PFWBA) algorithm are introduced in detail, focusing on the Prediction-based Fair Excessive Bandwidth Reallocation (PFEBR) algorithm with the Early Dynamic Bandwidth Allocation (E-DBA) mechanism and subsequent Dynamic Wavelength Allocation (DWA) scheme. For analyzing various wavelength allocation possibilities in Hybrid Passive Optical Networks (HPON) networks, a simulation program with the enhancement of the PFWBA algorithm is realized. Finally, a comparison of different methods of the wavelength allocation in conjunction with specific network traffic classes is executed for future HPON networks with considered protection schemes. Subsequently, three methods are presented from the viewpoint of HPON network traffic protection possibilities, including a new approach for the wavelength allocation based on network traffic protection assumptions.

Development of cryogenic undulators and measurement equipment for PETRA IV

DESY is planning to upgrade its current storage ring PETRA III into the ultra-low emittance photon source PETRA IV. The current H6BA cell design for the PETRA IV lattice will allow for insertion devices of up to 4 m length in the standard straight sections. In order to allow some beamlines to not only benefit from the performance improvements by the machine but also from the undulators, we started looking into using advanced undulator schemes as well. We decided to start the design of cryogenically cooled permanent magnet undulators (CPMUs) based on (Nd,Pr)FeB magnets for PETRA IV, since this technique has already been proven successfully by various other facilities. We will present the planned design for the magnet keepers, the pole tuning mechanism as well as a testbed for keepers under cryogenic conditions. We will also show a preliminary design for the in-situ Hall-probe bench currently developed for the upcoming PETRA IV CPMUs.

Spatio-Temporal Neural Network for Channel Prediction in Massive MIMO-OFDM Systems

In massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, a challenging problem is how to predict channel state information (CSI) (i.e., channel prediction) accurately in mobility scenarios. However, a practical obstacle is caused by CSI non-stationary and nonlinear dynamics in temporal domain. In this paper, we propose a spatio-temporal neural network (STNN) to achieve better performance by carefully taking into account the spatio-temporal characteristics of CSI. Specifically, STNN uses its encoder and decoder modules to capture the spatial correlation and temporal dependence of CSI. Further, the differencing-attention module is designed to deal with the non-stationary and nonlinear temporal dynamics and realize adaptive feature refinement for more accurate multi-step prediction. Additionally, an advanced training scheme is adopted to reduce the discrepancy between STNN training and testing. Evaluated on a realistic channel model with enhanced mobility and spherical waves, experimental results show that STNN can effectively improve the accuracy of prediction and perform well with respect to different signal to noise ratios (SNRs). Visualization and testing for unit root illustrate STNN is able to learn CSI time-varying patterns by alleviating series non-stationarity.

Finite-Control-Set Model Predictive Control of Permanent Magnet Synchronous Motor Drive Systems—An Overview

Permanent magnet synchronous motors (PMSMs) have been widely employed in the industry. Finite-control-set model predictive control (FCS-MPC), as an advanced control scheme, has been developed and applied to improve the performance and efficiency of the holistic PMSM drive systems. Based on the three elements of model predictive control, this paper provides an overview of the superiority of the FCS-MPC control scheme and its shortcomings in current applications. The problems of parameter mismatch, computational burden, and unfixed switching frequency are summarized. Moreover, other performance improvement schemes, such as the multi-vector application strategy, delay compensation scheme, and weight factor adjustment, are reviewed. Finally, future trends in this field is discussed, and several promising research topics are highlighted.

Distributed power flow controller based on fuzzy-logic controller for solar-wind energy hybrid system

The demand of electricity globally led to the concept of renewable energy resources for power generation that are eco-friendly and freely available from nature. The solar photovoltaic systems and wind-based power generators are considered as primary renewable resources and are called as Distributed Generation units as they are scattered in nature. These are operated with bidirectional converters by providing auxiliary services at grid side and load side in either mode of microgrid operation. Besides, the DC power generation units’ integration gets converted into AC system by means of inverters. These types of systems not only increase voltage and current harmonics, power frequency deviations but also drive the distribution system to risky operating zone. This emphasizes the stipulation of advanced control schemes for microgrid architecture. Consequently, power electronic converters introduce harmonics in the system and affect the system performance. To report these expanded issues, the authors recognized an advanced custom power device entitled distributed power flow controller. The proposed hybrid solar-wind energy system is first studied with a distributed power-flow controller. Later the system is examined by replacing proportional integral controller with fuzzy logic controller (FLC) for shunt control of distributed power-flow controller. To validate the investigations, MATLAB/Simulink software is used.

Theoretical study of the disc chopping apparatus of the feed chopper

The paper presents the results of a study in the kinematics and dynamics of a multi-blade disc cutting device. In the advanced hardware scheme, the clipping process is performed as punches. We have a straight blade edge rotated 200 degrees back from the radial direction.As a result, the sliding motion process is reduced and the trunk is easily cut. For this reason, it is advisable to reduce the sliding process when cutting a hard stem, and it is preferable that the blade be made in the form of a hyperbolic spiral, and not an Archimedes spiral, the curvature of which is directed towards the stem, and not in a straight line.Kinematic and dynamic studies performed on a multi-knife disk shearing device accelerate the technological process of the device, increase the pressure of the blade on the trunk, and make it possible to cut it intensively.

Exploiting Liquid Surface Tension in Microrobotics

Surface tension effects are known to be dominant at the submillimeter scale. Within this context, the literature has extensively described the underlying physics (e.g., surface tension, wetting, surface texturation, and coatings), and capillary forces have been exploited in a variety of applications (e.g., capillary picking, self-alignment, capillary sealing, and capillary bearings). As several stimuli can be used to control liquid menisci, these forces have been used mainly in microrobotics in open loop (i.e., without real-time feedback). However, at least two major sources of uncertainty hinder these forces from working properly in open loop: the variability due to contact-angle hysteresis (the difference between wetting and unwetting) and the variability in the involved volume of liquid. To be able to reject these disturbances, successful sensor integration and associated advanced control schemes need to be embedded in capillary microrobotic microsystems. This article analyzes research contributions in this field from three different perspectives: the stimulus action of the surface tension effect (light, B-field, etc.), the application field (actuation, picking, sealing, etc.), and the sensing and control schemes. Technologically complex developments coexist with elegant and straightforward engineering solutions. Biological aspects of surface tension are not included in this review.

Advanced Modulation Scheme of a Dual-Active-Bridge Series Resonant Converter (DABSRC) for Enhanced Performance

This paper proposes a two-degree-of-freedom (2-DoF) modulation technique for the efficient optimization of an open-loop three-phase dual-active-bridge series resonant converter (3P-DABSRC). The efficiency and performance of an conventional dual-active-bridge (DAB) converter decrease when it is operated over a wide range of voltage gain. The efficiency and performance of a DAB converter depend upon the switching and conduction losses. Circulating current is the main cause of conduction loss, and hard switching of active switches adds a switching loss. To increase the performance of DAB converters, the first objective is to minimize the conduction loss, and the second objective is to reduce the switching loss. Still, unfortunately, it is not easy to achieve these two objectives simultaneously. Circulating current helps us to reduce the switching loss, but the unbridled amount of circulating current will increase the root-mean-square inductor tank current, and as a result, the conduction loss will be increased. This paper presents an advanced modulation scheme for a 3P-DABSRC that can be used not only in low-power applications, but also in high-power applications. The DABSRC consists of a series LC resonant tank, isolated high-frequency transformer, and dual active bridge connected with the primary and secondary sides of the transformer. The proposed 2-DoF modulation technique not only minimizes the circulating current, but also eliminates the switching loss. Keeping the minimum phase shift between the primary and secondary bridges reduces the circulating current, and thus, all switches can be operated with zero-voltage switching (ZVS) for the entire power range. The power is controlled by changing the switching frequency from 45 to 63 kHz. To confirm the proposed topology and modulation scheme, a 1500 W DABSRC, which interfaces a 300 V DC bus with a 75 V DC bus, is simulated. A loss model of the proposed topology is also made to verify the results. The simulation results are used to confirm the proper operation of the 3P-DABSRC.