Miracle-dz/UniFAR-FTUs huggingface dataset:
Auxiliary Frequency Regulation of Power systems Considering Operational Reliability

Grid-connected renewable energy with high penetration rate puts forward higher requirements for power system frequency regulation. This paper designs a PI control loop based on the reliability index of Expected Energy not Supplied (EENS). The control loop is combined with the traditional primary and secondary frequency regulation model to realize the preventive adjustment of the active power output of conventional generators during normal operation. The proposed PI control loop takes the minimum EENS of the system as the control reference value, and the real-time prediction value of EENS as the feedback, and uses the deviation between the two as the basis to redistribute power outputs of dispatchable units. A method for determining feasible parameter ranges of the PI controller based on the Jury criterion is proposed. It is verified by simulation that the proposed control loop can effectively improve the frequency response effect.
Deep Reinforcement Learning Based Continuous Volt-Var Optimization in Power Distribution Systems with Renewable Energy Resources

In order to build a low-carbon and environmentally friendly energy system, a large number of distributed generation connected to the grid, which has brought great challenges to the stable operation of the distribution network. The intermittency and volatility of distributed generation lead to repeated voltage violations in power system. At the same time, the frequent switching of transformer taps and capacitor switches greatly increases the economic cost. This paper introduces the method of deep reinforcement learning to transform the Volt-Var optimize problem into a Markov decision process with continuous action space. The soft actor-critic algorithm is used to train the agent to control the static var compensator and the smart inverter to optimize the reactive power of the distribution network. Comparative studies on IEEE 12-node and 33-node test systems demonstrate the performance of the proposed solution.
Distributed State Estimation of Active Distribution Network Based on Probabilistic Forecasting and Community Detection Algorithm

This paper proposes a distributed state estimation method for active distribution network based on probabilistic forecasting and community detection algorithm. Firstly, Elman neural network combined with ameliorated gradient descent algorithm is utilized to generate power forecasts in the form of intervals with a certain confidence level, which effectively describes the uncertain characteristics of different types of distributed generations. Secondly, the distribution network is partitioned into several highly inter-cohesive subareas using community detection algorithm and community fusion methodology. Finally, numerical simulations are illustrated and discussed using a modified IEEE 123-node test system, which demonstrates the validity of the proposed distributed state estimation method.
Abnormal Message Logic & Sequence Detection for Process Layer Network in Digital Substation

Logic target attack and sequence target attack are two types of sophisticated and vicious cyber-attacks, which have not been paid a necessary attention in most critical part, i.e., the process layer network in digital substation. The relationships of such two types attack are explored, and networked structure of an intrusion detection & prevention system for the process layer network is proposed. A whitelist-based detection scheme is also proposed; firstly, the corresponding packets are described from the packet transfer path and the circuit breaker action packet; then, the different detection methods are used to check the packets; the simulation test results and verification show that the proposed method can detect an abnormal transmission path message and a replay attack with inserted circuit breaker action packet.
Decentralised Optimal Operation Strategy for AC/DC Distribution Networks Considering Electric Vehicles

With the development of Direct Current (DC) transmission technology and power electronics technology, traditional Alternating Current (AC) distribution networks are being gradually transformed into AC/DC hybrid distribution network with a significant zoning structure. This paper proposes a distributed optimal operation strategy for AC/DC distribution networks by considering electric vehicles (EVs). First, Monte Carlo simulation is used to establish a mathematical model for EVs. Considering the differences between constituent districts and the controllability of EVs, appropriate charging start times of EVs are designated, and the objective function is to minimize the fluctuation of the total load curve. Subsequently, the chance-constrained method is applied to address the uncertainty of renewable energy and load. By implementing the coordinated dispatch of source, load, and storage, the optimal operation model of the AC/DC distribution network is established, based on the improved alternating direction multiplier method. The simulation results verify the effectiveness of the proposed distributed optimal operation strategy.
Adaptive Robust Day-Ahead Optimal Dispatch for Power System with a High Penetration of Renewable Energy

In the power system with a high penetration of renewable energy, the joint optimal scheduling between wind-solar-storage hybrid power stations will facilitate the economic and effective operation of the system. In this paper, we propose the adaptive robust day-ahead energy-reserve joint optimal scheduling for the power system with a high penetration of new energy to address the uncertainties of new energy resources. First, with the minimum comprehensive daily operation cost as the objective function, we establish a three-layer robust adaptive optimal dispatch model for the power system while day-ahead energy-reserve dispatch is taken as the first-stage decision and the real-time power adjustment is the second-stage decision. Then, considering that there are no simple solutions to the newly established model, we use the uncertain scenario set to depict the uncertainty of the system, and introduce the auxiliary variable to represent the operation cost under the worst scenario with an attempt to decouple the three-layer optimal dispatch model and transform it into a single-layer robust optimization model with limited uncertain scenarios. Simulation results on the improved IEEE 30-node system validate the effectiveness of the proposed novel model and approach.
Analysis of Space Vector Modulation for Matrix Converter with Small Filter Components

Matrix converters (MC) often use space vector modulation (SVM) algorithm to calculate the duty cycle of each fundamental vector to generate the desired sinusoidal input current and output voltage, under the premise that the input side is an ideal voltage source, the output side is an ideal current source. However, the input filter capacitance and output filter inductance of MC in the actual system are both finite values, which do not strictly meet the above assumptions. In this paper, the SVM algorithm of MC is analyzed under the condition of small filter capacitance and filter inductance. It is found that the input side is not an ideal voltage source under the condition of small filter capacitance, and the output voltage will be affected by input voltage ripple. Under small output filter inductance, the output side is not an ideal current source, and the input current will be affected by the output current ripple. These influences are all manifested in the fact that the actual input current or output voltage is different from the expected value, that is, it contains low-frequency harmonics, which affects the power quality. Further, the validity conditions determined by the filter elements on the input side and output side are given. The validity of the analysis is verified in MATLAB /Simulink software.
Event-related desynchronization identification using continuous wavelet transform-SINGLE

Event-related desynchronization (ERD) and event-related potentials (ERPs) are two commonly observed phenomenons in the ongoing electroencephalogram (EEG) during internal or external triggered sensory, motor and cognitive events. Time-domain averaging is a widely used method to increase the signal-to-noise ratio and to detect some event-related cortical activities such as ERPs. However, this approach has drawbacks, where it's incapable of catching other types of events (e.g., ERD). To overcome this issue, we will consider an alternative approach. In this regard, time-frequency analysis (TFA) of the EEG using the continuous wavelet transform has been extensively used to explore different brain responses including ERPs and ERD.
Analysis and synthesis in the design of locomotor and spatial competences for a multisensory mobile robot

This dissertation reports on experiments in the design of a free-ranging robotic vehicle with locomotor and spatial skills. These two skills are suggested to be fundamental to any general mobile agent. No attempt has been made to incorporate manipulation, and motivational issues are not treated (the system's objectives are specified by a human). Two schools of thought with regard to robot design prevail: they are the representation-based or analytic, and representation-free or synthetic schools. These guiding philosophies are compared and contrasted in terms of their approaches to locomotion and localisation. In the course of this work, it has been found that the smoothness of a mobile robot's locomotion benefits from adjusting its speed to the density of obstacles it is encountering. A reactive, synthetic-type procedure for achieving this is offered, based on the frequency at which obstacle detection interrupts the robot's navigational system. However, it will be shown that a predictive, representation-based approach is more efficient. On the other hand, it will emerge that a very limited representation of the robot's operating environment and sensing systems is sufficient to allow the robot to localise accurately anywhere within its operating space, based only on the goodness of match between mapped and currently sensed data. The scheme treats the infrared, sonic, and ultrasonic sensors of an autonomous mobile robot as devices which partition its operating space qualitatively. It will be shown that the infrared and ultrasonic systems combined lead to position estimates accurate to within 10 cm of where the robot actually is, on average. These systems are found to be superior to the sonic system, surprisingly, because they have distinct regions within which they do not work: it is better to have accurate sensors with dead zones than inaccurate ones which can be used anywhere. Since it is not obvious which aspects of robot behaviour are well-suited, and which are unsuited for representation versus representation-free implementation, a conclusion drawn from the work is that elements of both approaches bear further consideration, and might be usefully combined.
Modelling and Simulation of Ge Absorber-based Tunnel Field-Effect Phototransistor at 1550 nm

In this article, a semi-analytical model for germanium absorber-based SOI-tunnel field-effect phototransistor at 1550 nm has been proposed. The model works efficiently for a wide range of intensities i.e. 10−2 to 102 µW/µm2 under the application of a very low gate voltage of 0.54 V. Exhaustive simulations using numerical ATLAS 2D device simulation tool with LUMNINOUS optical module have been carried out to propose the merit of tunnel field-effect transistors (TFETs) over metal oxide semiconductor field effect transistors (MOSFETs) for optical applications. The proposed TFET design provides a way to assist the ultrasensitive optical interconnects at a very low gate bias of 0.54 V with a maximum sensitivity of 1.5 × 107, responsivity of 270 mA/W, and detectivity of 3.5 × 1012 Jones at low power density of 0.01 µW/µm2. The TFET performance has further been improved by introducing a heavily doped pocket in between source and channel, which serves as a potential alternative for photodetector applications. The proposed pocket-doped TFET design offers significant improvements in device optical response with optimized sensitivity of 4.1 × 107, a responsivity of 772 mA/W, and a detectivity of 9.9 × 1012 Jones at an optical intensity of 0.01 µW/µm2, gate bias of 0.45 V, and drain voltage of 0.6 V. Transient analysis has been carried out to optimize the response time of the phototransistor for both Ge-gated conventional TFET and pocket-doped architecture and the fall time of ∼0.22 ms has been achieved.
Conductivity Behaviour under Pressure of Copper Micro-Additive/Polyurethane Composites (Experiment and Modelling)

In this study, micro-size copper particles (less than 25 μm) were incorporated into polyurethane (PU) using a solution mixing method and spin-coating technique to fabricate composite films in concentrations from 0.5 to 20 vol.%. The conductivity behaviour of these composites under pressure was studied experimentally and numerically. The conductivity measurements were performed in-plane and through-thickness under pressure. It was found that changes in conductivity only occurred in the z-direction under an applied pressure from 1 to 20 kPa. The results showed that pressure could induce conductivity up to about 7.2 × 10−1 S∙m−1 for composites with a Cu concentration higher than 2.6 vol.%. It seems that applied pressure reduced the thickness of the polymer film, decreasing the distance between copper particles and promoting the formation of a conductive network, thus making the material conductive. A semi-analytical model that can accurately provide the percolation threshold (PT) concentration was used to fit the experimental conductivity. The PT concentrations for PU-Cu composite ranged from 7.1 vol.% to 1.4 vol.% and decreased with the rise in pressure. This is known as a pressure-induced percolation transition phenomenon (PIPT). Finally, the finite element method based on the representative volume element model (FE-RVE) simulation technique was used to predict the conductivity behaviour. This numerical simulation provided a good description of the experimental conductivity after the PT and correctly predicted the PT concentration. This study shows that FE-RVE could be used to effectively simulate the influence of pressure on the electrical properties of a polymer–metal composite, reducing the need for costly and time-consuming experiments.
Design Governance: The CABE Experiment

© 2017 Taylor & Francis. All rights reserved. Design Governance focuses on how we design the built environment where most of us live, work, and play and the role of government in that process. To do so, it draws on the experience of the Commission for Architecture and the Built Environment (CABE), a decade-long, globally unique experiment in the governance of design. This book theorises design governance as an arm and aspiration of the state; tells the story of CABE, warts and all, and what came before and after; unpacks CABE's 'informal' toolbox: its methods and processes of design governance; and reflects on the effectiveness and legitimacy of design as a tool of modern-day government. The result is a new set of concepts through which to understand the governance of design as a distinct and important sub-field of urban design.
Interval early warning method for state of engineering structures based on structural health monitoring data

In order to carry out early warning for the abnormal state of engineering structures in time and optimize early warning interval (WI), a new construction of WI method based on relevance vector machine (RVM) and particle swarm optimization (PSO) under the framework of lower upper bound estimation (LUBE) is proposed. First, extract time‐domain features of structural monitoring data and then two RVM models are trained using time‐domain features to construct lower and upper bound of WI. Kernel parameters of RVM are optimized by PSO algorithm to minimize the loss function of LUBE and obtain the best optimal evaluation indices of WI. The LUBE based on PSO‐RVM interval warning method is verified through a numerical simulation and a case study on structural monitoring data of quayside container crane (QCC), and the performance of WI is compared with other prediction methods. The results indicate that the proposed construction of WI method can effectively improve the quality of WI, realize early warning damage, and provide better performance for early WI.
An Enhanced Power Sharing Strategy for Islanded Microgrids Considering Impedance Matching for Both Real and Reactive Power

There exists a strong coupling between real and reactive power owing to the complex impedances in droop based islanded microgrids (MGs). The existing virtual impedance methods consider improvements of the impedance matching for sharing of the voltage controlled power (VCP) (reactive power for Q - V droop, and real power for P - V droop), which yields a 1-DOF (degree of freedom) tunable virtual impedance. However, a weak impedance matching for sharing of the frequency controlled power (FCP) (real power for P -  droop, and reactive power for Q -  droop) may result in FCP overshoots and even oscillations during load transients. This in turn results in VCP oscillations due to the strong coupling. In this paper, a 2-DOF tunable adaptive virtual impedance method considering impedance matching for both real and reactive power (IM-PQ) is proposed to improve the power sharing performance of MGs. The dynamic response is promoted by suppressing the coupled power oscillations and power overshoots while realizing accurate power sharing. In addition, the proposed power sharing controller has a better parametric adaptability. The stability and dynamic performances are analyzed with a small-signal state-space model. Simulation and experimental results are presented to investigate the validity of the proposed scheme.
Bidirectional Dual Active Clamping Push-pull Dc-dc Converter

In this paper is presented a bidirectional dual active clamping push-pull dc-dc converter, to be used in applications requiring isolated bidirectional interface between two dc ports with low-voltage/low-current ripple. The main characteristics of the proposed converter are the non necessity of input and output filters in both ports, galvanic isolation, reduced number of magnetic components and high efficiency operation. The paper presents a theoretical analysis of the proposed converter and experimental analysis for a 600 W prototype, with 50 kHz switching frequency, and dc power supplies of 14 and 42 volts, at the input and output ports, respectively. The measured performance agreed well with the theoretical predictions.
Isolation and Characterization of Bacteriocin-Producing Lacticaseibacillus rhamnosus XN2 from Yak Yoghurt and Its Bacteriocin

Lactic acid bacteria (LAB) produce antimicrobial substances that could potentially inhibit the growth of pathogenic and food spoilage microorganisms. Lacticaseibacillus rhamnosus XN2, isolated from yak yoghurt, demonstrated antibacterial activity against Bacillus subtilis, B. cereus, Micrococcus luteus, Brochothrix thermosphacta, Clostridium butyricum, S. aureus, Listeria innocua CICC 10416, L. monocytogenes, and Escherichia coli. The antibacterial activity was estimated to be 3200 AU/mL after 30 h cultivation. Time-kill kinetics curve showed that the semi-purified cell-free supernatants (CFS) of strain XN2 possessed bactericidal activity. Flow cytometry analysis indicated disruption of the sensitive bacteria membrane by semi-purified CFS, which ultimately caused cell death. Interestingly, sub-lethal concentrations of semi-purified CFS were observed to reduce the production of α-haemolysin and biofilm formation. We further investigated the changes in the transcriptional level of luxS gene, which encodes signal molecule synthase (Al-2) induced by semi-purified CFS from strain XN2. In conclusion, L. rhamnosus XN2 and its bacteriocin showed antagonistic activity at both cellular and quorum sensing (QS) levels. Finally, bacteriocin was further purified by reversed-phase high-performance liquid chromatography (RP-HPLC), named bacteriocin XN2. The amino acid sequence was Met-Lue-Lys-Lys-Phe-Ser-Thr-Ala-Tyr-Val.
Massive MIMO 5G cellular networks: mm-wave versus mu-wave frequencies

Enhanced mobile broadband (eMBB) is one of the key use-cases for the development of the new standard 5G New Radio for the next generation of mobile wireless networks. Large-scale antenna arrays, a.k. a. Massive MIMO, the usage of carrier frequencies in the range 10-100 GHz, the so-called millimeter wave (mm-wave) band, and the network densification with the introduction of small-sized cells are the three technologies that will permit implementing eMBB services and realizing the Gbit/s mobile wireless experience. This paper is focused on the massive MIMO technology; initially conceived for conventional cellular frequencies in the sub-6 GHz range (\mu-wave), the massive MIMO concept has been then progressively extended to the case in which mm-wave frequencies are used. However, due to different propagation mechanisms in urban scenarios, the resulting MIMO channel models at \mu-wave and mm-wave are radically different. Six key basic differences are pinpointed in this paper, along with the implications that they have on the architecture and algorithms of the communication transceivers and on the attainable performance in terms of reliability and multiplexing capabilities.
Estimating High-Frequency Damping of a Beam through Electro-Mechanical Signatures of Piezoelectric Wafer Mounted on the Beam

The high-frequency electro-mechanical signatures, which are excited and received by piezoelectric wafers mounted on a beam, are sensitive to incipient defect in a beam. Predicting the sensing range of the piezoelectric wafers is needed to effectively conduct damage assessment of a beam through utilizing their advantage. Damping of a beam plays the most important role in determining the sensing range among other features. This paper has proposed a scheme for estimating high-frequency damping of a beam through electro-mechanical signatures of piezoelectric wafers mounted on the beam. Considering damping effect while resonance of a beam evolves, wave perspective is adopted to formulate the electro-mechanical signatures of piezoelectric wafers. The damping of a beam is estimated through the least squares method minimizing the difference between the calculated and the measured damping ratio function values which are obtained from formulated and measured electro-mechanical signatures, respectively. The validity of the proposed scheme has been demonstrated through numerical and experimental examples using an aluminum beam with collocated piezoelectric wafers.
Anatomical Engineering and 3D Printing for Surgery and Medical Devices: International Review and Future Exponential Innovations

Three-dimensional printing (3DP) has recently gained importance in the medical industry, especially in surgical specialties. It uses different techniques and materials based on patients' needs, which allows bioprofessionals to design and develop unique pieces using medical imaging provided by computed tomography (CT) and magnetic resonance imaging (MRI). Therefore, the Department of Biology and Medicine and the Department of Physics and Engineering, at the Bioastronautics and Space Mechatronics Research Group, have managed and supervised an international cooperation study, in order to present a general review of the innovative surgical applications, focused on anatomical systems, such as the nervous and craniofacial system, cardiovascular system, digestive system, genitourinary system, and musculoskeletal system. Finally, the integration with augmented, mixed, virtual reality is analyzed to show the advantages of personalized treatments, taking into account the improvements for preoperative, intraoperative planning, and medical training. Also, this article explores the creation of devices and tools for space surgery to get better outcomes under changing gravity conditions.