Upravljanje frekvencijom i radnom snagom mikro hidroelektrana kliznim režimom rada i redukcijom reda modela sustava

Micro hydro is treated as a major renewable energy resource. Such a kind of plants blooms because they can evade some dilemmas like population displacement and environmental problems. But their performance on the frequency index of power systems may be deteriorated in the presence of sudden small load perturbations and parameter uncertainties. To improve the performance, the problem of load frequency control (LFC) raises up. Design of state-based controllers on the aspect of modern control is challenging because only a part of the system states are measurable. This paper addresses the scheme of sliding mode control by model order reduction for the LFC problem of micro hydro power plants. The kind of plants usually has two operating modes, i.e., isolated mode and grid-connected mode. Under each operating mode, mathematical model and model reduction are investigated at first. According to the reduced-order model, a sliding mode control law is subsequently derived. Since the control law is applied to the original system, a sufficient condition about the system stability is proven in light of small gain theory. Simulation results illustrate the feasibility, validity and robustness of the presented scheme.Mikro hidroelektrane smatraju se jednim od glavnih obnovljivih izvora energije. Ovakve elektrane su poglavito zanimljive pošto izbjegavaju dileme vezane za iseljavanje ljudi i utjecaj na okoliš. Međutim, njihov učinak na indeks frekvencije energetskih sustava može biti negativan zbog naglih manjih promijena u opterećenju i nesigurnosti parametara. Kako bi se unaprijedila učinkovitost, javlja se problem regulacije frekvencije i radne snage. Projektiranje regulatora po varijablama stanja sustava izazovan je problem, jer je mjerljiv samo dio stanjasustava. Ovaj članak analizira problem upravljanja kliznim režimom rada reducirajući red modela sustava kod regulacije frekvencije i radne snage mikro hidroelektrana. Ovakve elektrane mogu raditi u samostalnom režimu rada ili biti spojene na distribucijsku mrežu. Za oba ražima rada prvo se istražuju matematički modeli te potom njihova redukcija. S obzirom na model reduciranog reda izvodi se upravljački zakon kliznog režima rada. Pošto se zakon upravljanja primjenjuje na izvorni sustav, dokazan je dovoljan uvjet za stabilnost u vidu teorije malog pojačanja.Simulacijski rezutati pokazuju izvedivost, ispravnost i robustnost predloženog pristupa

Nonlinear Fuzzy Model Predictive Control for a PWR Nuclear Power Plant

Reliable power and temperature control in pressurized water reactor (PWR) nuclear power plant is necessary to guarantee high efficiency and plant safety. Since the nuclear plants are quite nonlinear, the paper presents nonlinear fuzzy model predictive control (MPC), by incorporating the realistic constraints, to realize the plant optimization. T-S fuzzy modeling on nuclear power plant is utilized to approximate the nonlinear plant, based on which the nonlinear MPC controller is devised via parallel distributed compensation (PDC) scheme in order to solve the nonlinear constraint optimization problem. Improved performance compared to the traditional PID controller for a TMI-type PWR is obtained in the simulation

A3Graph : adversarial attributed autoencoder for graph representation learning

Recent years have witnessed a proliferation of graph representation techniques in social network analysis. Graph representation aims to map nodes in the graph into low-dimensional vector space while preserving as much information as possible. However, most existing methods ignore the robustness of learned latent vectors, which leads to inferior representation results due to sparse and noisy data in graphs. In this paper, we propose a novel framework, named A3Graph, which aims to improve the robustness and stability of graph representations. Specifically, we first construct an aggregation matrix by the combining positive point-wise mutual information matrix with the attribute matrix. Then, we enforce the autoencoder to reconstruct the aggregation matrix instead of the input attribute matrix. The enhancement autoencoder can incorporate structural and attributed information in a joint learning way to improve the noise-resilient during the learning process. Furthermore, an adversarial learning component is leveraged in our framework to impose a prior distribution on learned representations has been demonstrated as an effective mechanism in improving the robustness and stability in representation learning. Experimental studies on real-world datasets have demonstrated the effectiveness of the proposed A3Graph. © 2021 ACM

Mitochondrial dysfunction and therapeutic perspectives in osteoporosis

Osteoporosis (OP) is a systemic skeletal disorder characterized by reduced bone mass and structural deterioration of bone tissue, resulting in heightened vulnerability to fractures due to increased bone fragility. This condition primarily arises from an imbalance between the processes of bone resorption and formation. Mitochondrial dysfunction has been reported to potentially constitute one of the most crucial mechanisms influencing the pathogenesis of osteoporosis. In essence, mitochondria play a crucial role in maintaining the delicate equilibrium between bone formation and resorption, thereby ensuring optimal skeletal health. Nevertheless, disruption of this delicate balance can arise as a consequence of mitochondrial dysfunction. In dysfunctional mitochondria, the mitochondrial electron transport chain (ETC) becomes uncoupled, resulting in reduced ATP synthesis and increased generation of reactive oxygen species (ROS). Reinforcement of mitochondrial dysfunction is further exacerbated by the accumulation of aberrant mitochondria. In this review, we investigated and analyzed the correlation between mitochondrial dysfunction, encompassing mitochondrial DNA (mtDNA) alterations, oxidative phosphorylation (OXPHOS) impairment, mitophagy dysregulation, defects in mitochondrial biogenesis and dynamics, as well as excessive ROS accumulation, with regards to OP (Figure 1). Furthermore, we explore prospective strategies currently available for modulating mitochondria to ameliorate osteoporosis. Undoubtedly, certain therapeutic strategies still require further investigation to ensure their safety and efficacy as clinical treatments. However, from a mitochondrial perspective, the potential for establishing effective and safe therapeutic approaches for osteoporosis appears promising

Shifu2 : a network representation learning based model for advisor-advisee relationship mining

The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2. © 1989-2012 IEEE

Random Walks: A Review of Algorithms and Applications

A random walk is known as a random process which describes a path including a succession of random steps in the mathematical space. It has increasingly been popular in various disciplines such as mathematics and computer science. Furthermore, in quantum mechanics, quantum walks can be regarded as quantum analogues of classical random walks. Classical random walks and quantum walks can be used to calculate the proximity between nodes and extract the topology in the network. Various random walk related models can be applied in different fields, which is of great significance to downstream tasks such as link prediction, recommendation, computer vision, semi-supervised learning, and network embedding. In this paper, we aim to provide a comprehensive review of classical random walks and quantum walks. We first review the knowledge of classical random walks and quantum walks, including basic concepts and some typical algorithms. We also compare the algorithms based on quantum walks and classical random walks from the perspective of time complexity. Then we introduce their applications in the field of computer science. Finally we discuss the open issues from the perspectives of efficiency, main-memory volume, and computing time of existing algorithms. This study aims to contribute to this growing area of research by exploring random walks and quantum walks together.Comment: 13 pages, 4 figure

Predictive Variable Gain Iterative Learning Control for PMSM

A predictive variable gain strategy in iterative learning control (ILC) is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains

Anti-inflammatory and antioxidant traditional Chinese Medicine in treatment and prevention of osteoporosis

A metabolic bone disorder called osteoporosis is characterized by decreased bone mass and compromised microarchitecture. This condition can deteriorate bones and raise the risk of fractures. The two main causes of osteoporosis are an increase in osteoclast activity or quantity and a decrease in osteoblast viability. Numerous mechanisms, including estrogen shortage, aging, chemical agents, and decreased mechanical loads, have been linked to osteoporosis. Inflammation and oxidative stress have recently been linked to osteoporosis, according to an increasing number of studies. The two primary medications used to treat osteoporosis at the moment are bisphosphonates and selective estrogen receptor modulators (SERMs). These medications work well for osteoporosis brought on by aging and estrogen deprivation, however, they do not target inflammation and oxidative stress-induced osteoporosis. In addition, these drugs have some limitations that are attributed to various side effects that have not been overcome. Traditional Chinese medicine (TCM) has been applied in osteoporosis for many years and has a high safety profile. Therefore, in this review, literature related to botanical drugs that have an effect on inflammation and oxidative stress-induced osteoporosis was searched for. Moreover, the pharmacologically active ingredients of these herbs and the pathways were discussed and may contribute to the discovery of more safe and effective drugs for the treatment of osteoporosis