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A Three-Level Mechanism for Flexibility-Oriented Energy Management of Renewable-Based Community Microgrids with High Penetration of Smart Homes and Electric Vehicles

SSRN

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SSRN 2023年

作者： Mansouri, Seyed Amir Jordehi, Ahmad Rezaee Tostado-Véliz, Marcos Jurado, Francisco Northumbria University Electrical Power and Control Systems Research Group Ellison Place NE1 8ST Newcastle upon Tyne United Kingdom Department of Electrical Engineering Rasht Branch Islamic Azad University Rasht Iran Department of Electrical Engineering University of Jaén EPS Linares Jaén23700 Spain

We have developed a multi-level mechanism for flexibility-oriented energy management (FOEM) of the distribution system, in which the flexibility required by the main grid is provided through resources within renewable-based microgrids (RBMGs), including distributed generations (DGs), storage systems (SDs), internet-of-things enabled smart homes (IESHs), and plug-in electric vehicles (PEVs). In the first level, IESHs are programmed in a decentralized space subject to satisfying the residents' satisfaction index (SI). In the second level, RBMGs are programmed according to the announced plan of IESHs. Ultimately, in the third level, the operator of the main grid calculates the required flexibility capacities of the system according to the announced plans of RBMGs, where a risk-averse strategy is adopted to manage the risk of programming. Next, the main grid operator announces the required flexibility capacities to RBMGs and IESHs so that they can re-plan with the possibility of participating in flexibility markets. The numerical results clarify that the participation of RBMGs, IESHs and plug-in electric vehicles (PEVs) in providing flexibility capacities not only reduces the dependence of the distribution system on the upstream grid (UG), but also reduces the total daily costs by 30.7%. © 2023, The Authors. All rights reserved.

关键词： Energy management

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Fluorescence microscopic imaging of single desmin intermediate filaments elongated by the presence of divalent cations in vitro

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Biophysical Chemistry 2022年 287卷 106839页

作者： Sato, Masashi Ishizaka, Takumi Hotta, Jun-ichi Hatori, Kuniyuki Department of Bio-Systems Engineering Graduate School of Science and Engineering Yamagata University 4-3-16 Jyonan Yonezawa 992-8510 Japan Department of Mechanical Systems Engineering Faculty of Engineering Yamagata University 4-3-16 Jyonan Yonezawa 992-8510 Japan Department of Applied Chemistry Chemical Engineering and Biochemical Engineering Graduate School of Science and Engineering Yamagata University 4-3-16 Jyonan Yonezawa 992-8510 Japan

The formation of intermediate filaments (IFs), a paradigmatic assembly system in biological macromolecules, depends on cations. Herein, to explore the combined effect of ionic strength and divalent cations, we used fluorescence microscopy and examined the in vitro effects of MgCl₂, CaCl₂, and SrCl₂ on the KCl concentration-dependent growth of desmin IFs. Fluorescently-labeled desmin IF assembly initiated by KCl and 5 mM divalent cations led to the formation of single desmin IFs in the KCl concentration range of 25–50 mM. Addition of divalent cations resulted in increased fluorescence intensity in the filament images. KCl concentrations lower or higher than the aforementioned range resulted in the induction of networks of entangled IFs, which were visualized at high resolution via direct stochastic optical reconstruction microscopy. These findings provide insights into the versatility of the IF assembly mechanism and the optimization of fluorescence microscopy of single desmin IFs. © 2022 Elsevier B.V.

关键词： Cytoskeleton Electrostatic interaction Intermediate filament Self-assembly Super-resolution microscopy

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The Impact of a Novel Fe-Btc/Biochar Material on Nitrogen Conversion in Electric Field Compost and its Microbial Driving Mechanism

SSRN

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SSRN 2024年

作者： Zhang, Yang Zhang, Dongli Ding, Jingtao Zhou, Haibin Cai, Huiwu Zhou, Wenying Cheng, Hongsheng Feng, Qikun Cheng, Qingyi Zhang, Yun Zhang, Pengyue Ma, Shuangshuang Shen, Yujun Institute of Energy and Environmental Protection Academy of Agricultural Planning & Engineering Ministry of Agriculture and Rural Affairs Beijing100125 China Key Laboratory of Technologies and Models for Cyclic Utilization from Agricultural Resources Ministry of Agriculture and Rural Affairs Beijing100125 China School of Chemistry and Chemical Engineering Xi’an University of Science & Technology Xi’an710054 China State Key Laboratory of Power Systems Department of Electrical Engineering Tsinghua University Beijing100084 China

Electric field-assisted composting technology is a novel and efficient approach to composting. however, it has various challenges, such as the emission of nitrogenous gases and limited electrical conductivity. The development of novel additives is a potential way to resolve this problem. In this study, a novel additive Fe-BTC/BC was developed and its effects on nitrogenous gas emissions was examined during electric field-assisted composting. The result indicated that incorporating Fe-BTC/BC reduced NH3 and N2O emissions by 47.05% and 19.83%, respectively, compared with that of the control, and led to an increase in peak temperature from 66.4 ℃ to 71.2 ℃ and a shortened composting period from 16 d to 9 d. Microbiological analysis indicated that Fe-BTC/BC enhanced the abundance of nitrifying functional microbes, such as Bacillus (from 24.09% to 27.18%), and reduced that of denitrifying functional microbes (from 5.08% to 3.91%). RDA indicated that NH3 emission is in connection with nitrifying microbes (Clostridium_sensu_stricto_1, Corynebacterium). In addition, reduction-oxidation (REDOX) activity was enhanced and electron transfer was accelerated upon the addition of Fe-BTC/BC. These findings indicate that Fe-BTC/BC has a synergistic effect that improves nitrogen conversion efficiency by altering the abundance of microorganisms and accelerating electron transfer. This study provides an important scientific basis for the development of novel materials to reduce the emission of nitrogenous gases during electric field-assisted composting. © 2024, The Authors. All rights reserved.

关键词： Electric fields

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Adaptive Fuzzy Dynamic Surface control of Flexible-Joint Robot systems With Input Saturation

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 97-107页

作者： Song Ling Huanqing Wang Peter X.Liu the School of Mechanical Electronic and Control EngineeringBeijing Jiaotong University the Department of Mathematics Bohai University IEEE the Department of Systems and Computer Engineering Carleton University

In this paper, we propose an adaptive fuzzy dynamic surface control(DSC) scheme for single-link flexible-joint robotic systems with input saturation. A smooth function is utilized with the mean-value theorem to deal with the difficulties associated with input saturation. An adaptive DSC design with an auxiliary first-order filter is used to solve the "explosion of complexity"problem. It is proved that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, and the tracking error eventually converges to a small neighborhood around zero. The main advantage of the proposed method is that only one adaptation parameter needs to be updated,which reduces the computational burden significantly. Simulation results demonstrate the feasibility of the proposed scheme and the comparison results show that the improved DSC method can reduce the computational burden by almost two thirds in comparison with the standard DSC method.

关键词： Adaptive fuzzy control dynamic surface control (DSC) flexible-joint (FJ) robots single-link tracking control

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Real-time multiple model joint estimation for an urban traffic junction subject to jump dynamics

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IFAC-PapersOnLine 2020年第2期53卷 14984-14989页

作者： Luana Chetcuti Zammit Simon G. Fabri Kenneth Scerri Department of Systems and Control Engineering University of Malta

Traffic conditions in signalized junctions are highly dynamic and may be subject to abrupt changes due to unanticipated traffic incidents or network obstructions. These abrupt changing conditions are represented as different regimes or modes where each mode is represented by its own distinct model, forming a set of multiple models. At any instance in time, only one model of the set has the potential of representing the physical system dynamics at that time. However the dynamics may arbitrarily jump over to a different regime when an abnormal condition arises. Furthermore, it might be impossible to identify these models a priori . Hence, a multiple model approach is developed to self-detect these abrupt changes, identify which member of the set best represents the actual system and automatically self-configure and add a new model to the set when a previously unmodelled regime arises. This approach makes use of a real-time joint (dual) estimation algorithm to estimate traffic state variables such as queue lengths and traffic flow, as well as model parameters such as turning ratios, saturation flow values and noise covariance resulting from unmodelled dynamics and measurement errors. The proposed algorithm is validated through simulations on signalized 3-arm and 4-arm junctions with typical day-to-day traffic conditions including several network irregularities occuring at different times of the day such as arm closures as a result of traffic incidents. This work is aimed to form part of adaptive control loops for traffic light systems that are able to autonomously adjust to changing traffic conditions so as to ensure efficient vehicle flows.

关键词： intelligent traffic systems multiple model estimation jump dynamics online estimation self-detection self-configuration

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Pre-evacuation path planning in chemicals-concentrated areas with risk assessment and individual character analysis

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Journal of Loss Prevention in the Process Industries 2023年第1期83卷

作者： Dou, Zhan Hu, Yuan-Hao Mebarki, Ahmed Zhang, Jian-Wen Wang, Qian-Lin Chen, Liang-Chao Shu, Chi-Min Department of Safety Engineering College of Mechanical and Electrical Engineering Beijing University of Chemical Technology Beijing100029 China 5 bd Descartes Marne-la-Vallee77454 France Jiangsu Key Laboratory of Hazardous Chemicals Safety and Control College of Safety Science and Engineering Nanjing Tech University Jiangsu Nanjing211816 China Process Safety and Disaster Prevention Laboratory National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan

With the ever-increasing development of those chemical parks (concentrated areas), the inherent hazards may remain the major leading cause of serious casualties, causing dramatic increases in deaths and injuries. Despite this, proper path beforehand can effectively minimise the number of deaths or injured. In this study, in order to better address the aforesaid issue, the pre-evacuation path planning was adopted to do so. This method can serve to prepare emergency response in case of extreme events, such as fires, explosions, or dangerous leakages, because these accidents could happen in chemical parks (concentrated areas). To that end, a framework was therefore proposed. First, the general risk representation was conducted. After the main hazards as well as the vulnerability within the facilities was identified, the interaction between those two factors could be expressed with matrices. This was followed by the analysis of the domino effect, which tends to occur under such circumstances. Second, individuals' visibility and inclination at each location to choose the nearest exit gate or shelter zone were analyzed by space syntax analysis. Third, a weighted risk map mainly composed of risk, individual's visibility, and inclination of exits was therefore generated. And the lowest cumulative risk path was simulated and analyzed accordingly. Finally, the map modified with received risks suggests that each individual's safest route from their current locations can be possibly simulated with Dijkstra's algorithm, which corresponds to the lowest cumulative risk. For the purposes of illustration and validation, a real case was adopted. The results demonstrated that this framework could provide both technical and theoretical support for the pre-evacuation path planning in chemicals-concentrated areas like chemicals-concentrated areas. © 2023 Elsevier Ltd

关键词： Visibility

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Extracting Stress-Related EEG Patterns from Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency

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IEEE Transactions on Neural systems and Rehabilitation engineering 2024年 32卷 1817-1827页

作者： Su, Cheng-Hua Ko, Li-Wei Jung, Tzyy-Ping Onton, Julie Tzou, Shey-Cherng Juang, Jia-Chi Hsu, Chung-Yao Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Department of Biomedical Science and Environmental Biology Kaohsiung807 Taiwan University of California San Diego Swartz Center for Computational Neuroscience La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan University of California San Diego Institute for Neural Computation La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Biological Science and Technology Hsinchu300 Taiwan Kaohsiung Medical University Hospital Sleep Disorders Center Department of Neurology Kaohsiung807 Taiwan Kaohsiung Medical University College of Medicine Department of Neurology Kaohsiung807 Taiwan

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution. © 2001-2011 IEEE.

关键词： Entropy

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Characterization of QUBO reformulations for the maximum k-colorable subgraph problem

arXiv

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arXiv 2021年

作者： Quintero, Rodolfo Bernal, David Terlaky, Tamás Zuluaga, Luis F. Industrial and Systems Engineering Lehigh University United States Department of Chemical Engineering Carnegie Mellon University United States

Quantum devices can be used to solve constrained combinatorial optimization (COPT) problems thanks to the use of penalization methods to embed the COPT problem’s constraints in its objective to obtain a quadratic unconstrained binary optimization (QUBO) reformulation of the COPT. However, the particular way in which this penalization is carried out, affects the value of the penalty parameters, as well as the number of additional binary variables that are needed to obtain the desired QUBO reformulation. In turn, these factors substantially affect the ability of quantum computers to efficiently solve these constrained COPT problems. This efficiency is key towards the goal of using quantum computers to solve constrained COPT problems more efficiently than with classical computers. Along these lines, we consider an important constrained COPT problem;namely, the maximum k-colorable subgraph (MkCS) problem, in which the aim is to find an induced k-colorable subgraph with maximum cardinality in a given graph. This problem arises in channel assignment in spectrum sharing networks, VLSI design, human genetic research, and cybersecurity. We derive two QUBO reformulations for the MkCS problem, and fully characterize the range of the penalty parameters that can be used in the QUBO reformulations. Further, one of the QUBO reformulations of the MkCS problem is obtained without the need to introduce additional binary variables. To illustrate the benefits of obtaining and characterizing these QUBO reformulations, we benchmark different QUBO reformulations of the MkCS problem by performing numerical tests on D-Wave’s quantum annealing devices. These tests also illustrate the numerical power gained by using the latest D-Wave’s quantum annealing *** Codes 68Q12, 81P68 Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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Adaptive PI-Fuzzy Like control of a Stack Pneumatic Actuators Testbed for Multi-Configuration Small Scale Soft Robotics

Adaptive PI-Fuzzy Like Control of a Stack Pneumatic Actuator...

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International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS)

作者： Ammar K. Al Mhdawi Nick Wright Amjad J. Humaidi Ahmad Taher Azar Department of Electrical and Electronic Engineering Newcastle University Newcastle UK Edge Hill University UK ASSCL Lab Prince Sultan University KSA Control and Systems Engineering Department University of Technology Baghdad Iraq College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Egypt

It has become evident that pneumatic muscle-based robots are the most compliant of all soft and semi-soft robotic testbeds under investigation, despite the fact that the artificial muscle-based robot is the most challenging due to its hyperelastic behavior resulting from the constitutive elastic material. The purpose of this paper is to examine the development of a physical pneumatic actuation testbed and the application of control strategies for precision small scale soft robotics control. Moreover, an adaptive PI-Fuzzy like control is proposed for reference signal tracking. It is imperative to note that in this application, the controller's performance is determined by its ability to track a reference signal as well as its ability to minimize the absolute tracking error. The Fuzzy Logic controller (FLC) controller, including the nature of the membership functions and the rule base, is described in detail. Simulations and hardware testing have revealed that the adaptive PI-Fuzzy like controller outperforms the traditional FLC controller in terms of experimental results. Additionally, the actuation speed is controlled under different loading conditions. It is considered that the proposed testbed is capable of controlling and managing multi-configurations of medium to small scale soft-based robots efficiently and effectively.

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A Noise Mitigation Approach for Improving Position Estimate in Indoor Vehicle Navigation Using Inertial Measurement Unit

A Noise Mitigation Approach for Improving Position Estimate ...

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control Conference (ICC), 2019 Sixth Indian

作者： Vasumathi R Subhadeep Kumar Ramkrishna Pasumarthy Nirav Bhatt Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Indian Institute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence and Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India Department of Biotechnology Robert Bosch Center of Data Sciences and Artificial Intelligence Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India

Accurate position estimation, especially in indoor environments, presents a formidable challenge despite significant advancements in estimation methodologies. The integration of additional sensors is often imperative to refine estimates, particularly within indoor settings. The choice of additional sensors depends on the specific applications and operational range. This study focuses on devising a position estimation framework utilizing the internal sensors such as the Inertial Measurement Unit (IMU) and wheel encoders within a scaled-down Electric Vehicle. The proposed approach entails a combination of crafting a digital filter and implementing a Kalman filter to effectively attenuate noise and errors in the IMU data, thereby enhancing the position estimate, and the methodology is validated through experiments. This work contributes to the broader discourse on advancing indoor position estimation while bypassing the need for external positioning systems.

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