Distributed Control
Dotaz
Zobrazit nápovědu
This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.
- Klíčová slova
- collaboration, distributed processing, human–robot interaction, obstacles detection, sensors network, workspace monitoring,
- MeSH
- počítačové komunikační sítě * MeSH
- Publikační typ
- časopisecké články MeSH
In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG's and improve the robustness of the system under varying load and generation conditions. Simulation findings under different tests have shown significant improvements in performance and computational simulation. The rise time is reduced by 60%, the overshoot is reduced by 80%, the THD of the voltage is reduced by 75% (from 0.99 to 0.20%), and the THD of the current is reduced by 69% (from 10.73 to 3.36%) compared to the conventional PI controller technique. Furthermore, voltage and current THD values were maintained below the IEEE-519 standard limits of 5% and 8%, respectively, for the power quality enhancement. Fluctuations in voltage and frequency were also maintained at 2% tolerance and 1% tolerance, respectively, across all voltage limits, which is consistent with international norms. Power-sharing errors were reduced by 50% after conducting the robustness tests against the DC supply and load disturbances. In addition, the proposed strategy outperforms the previous control techniques presented at the state of the art in terms of adaptability, stability and, especially, the ability to reduce the THD, which validates its effectiveness for MG systems control and optimization under uncertain conditions.
The relative contribution of top-down and bottom-up processes regulating primary decomposers can influence the strength of the link between the soil animal community and ecosystem functioning. Although soil bacterial communities are regulated by bottom-up and top-down processes, the latter are considered to be less important in structuring the diversity and functioning of fungal-dominated ecosystems. Despite the huge diversity of mycophagous (fungal-feeding) soil fauna, and their potential to reverse the outcomes of competitive fungal interactions, top-down grazing effects have never been found to translate to community-level changes. We constructed soil mesocosms to investigate the potential of isopods grazing on cord-forming basidiomycete fungi to influence the community composition and functioning of a complex woodland soil microbial community. Using metagenomic sequencing we provide conclusive evidence of direct top-down control at the community scale in fungal-dominated woodland soil. By suppressing the dominant cord-forming basidiomycete fungi, isopods prevented the competitive exclusion of surrounding litter fungi, increasing diversity in a community containing several hundred fungal species. This isopod-induced modification of community composition drove a shift in the soil enzyme profile, and led to a restructuring of the wider mycophagous invertebrate community. We highlight characteristics of different soil ecosystems that will give rise to such top-down control. Given the ubiquity of isopods and basidiomycete fungi in temperate and boreal woodland ecosystems, such top-down community control could be of widespread significance for global carbon and nutrient cycling.
- MeSH
- fungální proteiny genetika metabolismus MeSH
- houby klasifikace enzymologie fyziologie MeSH
- Isopoda fyziologie MeSH
- půda chemie MeSH
- půdní mikrobiologie * MeSH
- regulace genové exprese enzymů MeSH
- regulace genové exprese u hub MeSH
- zvířata MeSH
- Check Tag
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- fungální proteiny MeSH
- půda MeSH
Industrial process tomography offers two key advantages over conventional sensing systems. Firstly, process tomography systems provide information about 2D or 3D distributions of the variables of interest. Secondly, tomography looks inside the processes without penetrating them physically, i.e., sensing is possible despite harsh process conditions, and the operation of the process is not disturbed by intrusive sensors. These advantages open new perspectives for the field of process control, and the potential of closed-loop control applications is one of the main driving forces behind the development of industrial tomography. Despite these advantages and decades of development, closed-loop control applications of tomography are still not really common. This article provides an overview of the current state-of-the-art in the field of control systems with tomographic sensors. An attempt is made to classify the different control approaches, critically assess their strengths and weak points, and outline which directions may lead to increased future utilization of industrial tomography in the closed-loop feedback control.
Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi-feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi-feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi-feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.
- Klíčová slova
- Backward NN, Communication latencies, Distributed control, Multi-level control, NN microgrid control, Renewable energy sources,
- Publikační typ
- časopisecké články MeSH
Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.
- Klíčová slova
- Epilepsy, deep brain stimulation, distributed computing, implantable devices, seizure detection, seizure prediction,
- Publikační typ
- časopisecké články MeSH
The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support Vector Regression (SVR), to enhance the efficiency and reliability of these systems. The proposed SVR algorithm leverages comprehensive historical energy production data, detailed weather patterns, and dynamic grid conditions to accurately forecast power generation. Our model demonstrated significantly lower error metrics compared to traditional linear regression models, achieving a Mean Squared Error of 2.002 for solar PV and 3.059 for wind power forecasting. The Mean Absolute Error was reduced to 0.547 for solar PV and 0.825 for wind scenarios, and the Root Mean Squared Error (RMSE) was 1.415 for solar PV and 1.749 for wind power, showcasing the model's superior accuracy. Enhanced predictive accuracy directly contributes to optimized resource allocation, enabling more precise control of energy generation schedules and reducing the reliance on external power sources. The application of our SVR model resulted in an 8.4% reduction in overall operating costs, highlighting its effectiveness in improving energy management efficiency. Furthermore, the system's ability to predict fluctuations in energy output allowed for adaptive real-time energy management, reducing grid stress and enhancing system stability. This approach led to a 10% improvement in the balance between supply and demand, a 15% reduction in peak load demand, and a 12% increase in the utilization of renewable energy sources. Our approach enhances grid stability by better balancing supply and demand, mitigating the variability and intermittency of renewable energy sources. These advancements promote a more sustainable integration of renewable energy into the microgrid, contributing to a cleaner, more resilient, and efficient energy infrastructure. The findings of this research provide valuable insights into the development of intelligent energy systems capable of adapting to changing conditions, paving the way for future innovations in energy management. Additionally, this work underscores the potential of machine learning to revolutionize energy management practices by providing more accurate, reliable, and cost-effective solutions for integrating renewable energy into existing grid infrastructures.
- Klíčová slova
- Artificial intelligence, Cognitive science, Distributed generation, Energy management, Microgrid, Optimization, Predictive modeling, Renewable energy, Support vector regression,
- Publikační typ
- časopisecké články MeSH
Albedo-a primary control on surface melt-varies considerably across the Greenland Ice Sheet yet the specific surface types that comprise its dark zone remain unquantified. Here we use UAV imagery to attribute seven distinct surface types to observed albedo along a 25 km transect dissecting the western, ablating sector of the ice sheet. Our results demonstrate that distributed surface impurities-an admixture of dust, black carbon and pigmented algae-explain 73% of the observed spatial variability in albedo and are responsible for the dark zone itself. Crevassing and supraglacial water also drive albedo reduction but due to their limited extent, explain just 12 and 15% of the observed variability respectively. Cryoconite, concentrated in large holes or fluvial deposits, is the darkest surface type but accounts for <1% of the area and has minimal impact. We propose that the ongoing emergence and dispersal of distributed impurities, amplified by enhanced ablation and biological activity, will drive future expansion of Greenland's dark zone.
- MeSH
- ledový příkrov * MeSH
- monitorování životního prostředí MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Grónsko MeSH
- Klíčová slova
- PHARMACY/history *,
- MeSH
- dějiny lékárnictví * MeSH
- farmaceutické služby * MeSH
- kontrola léčiv a omamných látek * MeSH
- lékárny * MeSH
- lidé MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: CYP2C9*3 allele has been reported to correlate with increased plasma concentration of fluvastatin active form in healthy volunteers. We analyzed the correlation between the CYP2C9 genotype and cholesterol-lowering effect of fluvastatin in human hypercholesterolemic patients. MATERIAL/METHODS: The study was prospective, without any interventions to standard procedures of hypolipidemic treatment. CYP2C9 genotype was determined by PCR-RFLP assay in 87 patients on concomitant fluvastatin therapy, in 48 patients on monotherapy, and in a control group of 254 healthy volunteers of Czech nationality. Biochemical and clinical data were collected before the initiation of fluvastatin treatment and 12 weeks later. RESULTS: The frequency of CYP2C9 alleles did not differ significantly among groups of patients and volunteers. The most frequently observed allele was CYP2C9*2. Treatment with 80 mg of fluvastatin daily of 48 patients on monotherapy for 12 weeks resulted in mean low-density lipoprotein cholesterol (LDL-C) reduction by 25%, mean serum total cholesterol (TC) reduction by 21%, and mean triglyceride (TG) reduction by 28%. The CYP2C9*1/*3 genotype was associated with a decrease in LDL-C levels (by 40.0% for CYP2C9*1/*3, but only by 22.4% for CYP2C9*1/*1), and with the reduction of TC (by 28.6% in CYP2C9*1/*3 versus 20.2% in CYP2C9*1/*1). CONCLUSIONS: In hypercholesterolemic patients, LDL-C serum concentration was decreased more significantly in fluvastatin-treated subjects bearing the CYP2C9*1/*3 genotype compared to CYP2C9*1/*1 genotype. However, due to rare occurrence of some CYP genotypes, it was impossible to report a definitive positive genotype-fluvastatin effect association.
- MeSH
- alely MeSH
- anticholesteremika škodlivé účinky farmakologie MeSH
- aromatické hydroxylasy genetika MeSH
- cholesterol krev MeSH
- cytochrom P450 CYP2C9 MeSH
- demografie MeSH
- dospělí MeSH
- fluvastatin MeSH
- frekvence genu genetika MeSH
- genotyp MeSH
- hypercholesterolemie krev farmakoterapie genetika MeSH
- indoly škodlivé účinky farmakologie MeSH
- jednonukleotidový polymorfismus genetika MeSH
- kyseliny mastné mononenasycené škodlivé účinky farmakologie MeSH
- lidé středního věku MeSH
- lidé MeSH
- prevalence MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- studie případů a kontrol MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Geografické názvy
- Československo MeSH
- Názvy látek
- anticholesteremika MeSH
- aromatické hydroxylasy MeSH
- cholesterol MeSH
- CYP2C9 protein, human MeSH Prohlížeč
- cytochrom P450 CYP2C9 MeSH
- fluvastatin MeSH
- indoly MeSH
- kyseliny mastné mononenasycené MeSH
