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1.
PLoS One ; 19(2): e0296979, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38335185

RESUMO

With the rapid development of smart grids, society has become increasingly urgent to solve the problems of low energy utilization efficiency and high energy consumption. In this context, load identification has become a key element in formulating scientific and effective energy consumption plans and reducing unnecessary energy waste. However, traditional load identification methods mainly focus on known electrical equipment, and accurate identification of unknown electrical equipment still faces significant challenges. A new encoding feature space based on Triplet neural networks is proposed in this paper to detect unknown electrical appliances using convex hull coincidence degree. Additionally, transfer learning is introduced for the rapid updating of the pre-classification model's self-incrementing class with the unknown load. In experiments, the effectiveness of our method is successfully tested on the PLAID dataset. The accuracy of unknown load identification reached 99.23%. Through this research, we expect to bring a new idea to the field of load identification to meet the urgent need for the identification of unknown electrical appliances in the development of smart grids.


Assuntos
Síndromes Periódicas Associadas à Criopirina , Aprendizado Profundo , Humanos , Sistemas Computacionais , Eletricidade , Fadiga
2.
Sensors (Basel) ; 24(3)2024 Jan 28.
Artigo em Inglês | MEDLINE | ID: mdl-38339568

RESUMO

This study is related to Smart Aqua Farm, which combines artificial intelligence (AI) and Internet of things (IoT) technology. This study aimed to monitor fish growth in indoor aquaculture while automatically measuring the average size and area in real time. Automatic fish size measurement technology is one of the essential elements for unmanned aquaculture. Under the condition of labor shortage, operators have much fatigue because they use a primitive method that samples the size and weight of fish just before fish shipment and measures them directly by humans. When this kind of process is automated, the operator's fatigue can be significantly reduced. Above all, after measuring the fish growth, predicting the final fish shipment date is possible by estimating how much feed and time are required until the fish becomes the desired size. In this study, a video camera and a developed light-emitting grid panel were installed in indoor aquaculture to acquire images of fish, and the size measurement of a mock-up fish was implemented using the proposed method.


Assuntos
Aquicultura , Inteligência Artificial , Humanos , Animais , Aquicultura/métodos , Peixes , Sistemas Computacionais , Tecnologia
3.
Sensors (Basel) ; 24(3)2024 Jan 31.
Artigo em Inglês | MEDLINE | ID: mdl-38339638

RESUMO

In the field of unmanned systems, the combination of artificial intelligence with self-operating functionalities is becoming increasingly important. This study introduces a new method for autonomously detecting humans in indoor environments using unmanned aerial vehicles, utilizing the advanced techniques of a deep learning framework commonly known as "You Only Look Once" (YOLO). The key contribution of this research is the development of a new model (YOLO-IHD), specifically designed for human detection in indoor using drones. This model is created using a unique dataset gathered from aerial vehicle footage in various indoor environments. It significantly improves the accuracy of detecting people in these complex environments. The model achieves a notable advancement in autonomous monitoring and search-and-rescue operations, highlighting its importance for tasks that require precise human detection. The improved performance of the new model is due to its optimized convolutional layers and an attention mechanism that process complex visual data from indoor environments. This results in more dependable operation in critical situations like disaster response and indoor rescue missions. Moreover, when combined with an accelerating processing library, the model shows enhanced real-time detection capabilities and operates effectively in a real-world environment with a custom designed indoor drone. This research lays the groundwork for future enhancements designed to significantly increase the model's accuracy and the reliability of indoor human detection in real-time drone applications.


Assuntos
Inteligência Artificial , Dispositivos Aéreos não Tripulados , Humanos , Reprodutibilidade dos Testes , Sistemas Computacionais , Cultura
4.
Nat Commun ; 15(1): 389, 2024 Jan 09.
Artigo em Inglês | MEDLINE | ID: mdl-38195598

RESUMO

Structural biology efforts using cryogenic electron microscopy are frequently stifled by specimens adopting "preferred orientations" on grids, leading to anisotropic map resolution and impeding structure determination. Tilting the specimen stage during data collection is a generalizable solution but has historically led to substantial resolution attenuation. Here, we develop updated data collection and image processing workflows and demonstrate, using multiple specimens, that resolution attenuation is negligible or significantly reduced across tilt angles. Reconstructions with and without the stage tilted as high as 60° are virtually indistinguishable. These strategies allowed the reconstruction to 3 Å resolution of a bacterial RNA polymerase with preferred orientation, containing an unnatural nucleotide for studying novel base pair recognition. Furthermore, we present a quantitative framework that allows cryo-EM practitioners to define an optimal tilt angle during data acquisition. These results reinforce the utility of employing stage tilt for data collection and provide quantitative metrics to obtain isotropic maps.


Assuntos
Benchmarking , Sistemas Computacionais , Microscopia Crioeletrônica , Anisotropia , Coleta de Dados
5.
Sci Rep ; 14(1): 752, 2024 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-38191897

RESUMO

Climate change and human activity threaten sea turtle nesting beaches through increased flooding and erosion. Understanding the environmental characteristics that enable nesting can aid to preserve and expand these habitats. While numerous local studies exist, a comprehensive global analysis of environmental influences on the distribution of sea turtle nesting habitats remains largely unexplored. Here, we relate the distribution of global sea turtle nesting to 22 coastal indicators, spanning hydrodynamic, atmospheric, geophysical, habitat, and human processes. Using state-of-the-art global datasets and a novel 50-km-resolution hexagonal coastline grid (Coastgons), we employ machine learning to identify spatially homogeneous patterns in the indicators and correlate these to the occurrence of nesting grounds. Our findings suggest sea surface temperature, tidal range, extreme surges, and proximity to coral and seagrass habitats significantly influence global nesting distribution. Low tidal ranges and low extreme surges appear to be particularly favorable for individual species, likely due to reduced nest flooding. Other indicators, previously reported as influential (e.g., precipitation and wind speed), were not as important in our global-scale analysis. Finally, we identify new, potentially suitable nesting regions for each species. On average, [Formula: see text] of global coastal regions between [Formula: see text] and [Formula: see text] latitude could be suitable for nesting, while only [Formula: see text] is currently used by turtles, showing that the realized niche is significantly smaller than the fundamental niche, and that there is potential for sea turtles to expand their nesting habitat. Our results help identify suitable nesting conditions, quantify potential hazards to global nesting habitats, and lay a foundation for nature-based solutions to preserve and potentially expand these habitats.


Assuntos
Antozoários , Tartarugas , Humanos , Animais , Mudança Climática , Sistemas Computacionais , Inundações
6.
Sci Rep ; 14(1): 850, 2024 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-38191773

RESUMO

Winter Storm Uri was a disaster that impacted much of the United States during February of 2021. During and after the storm, Texas and Oklahoma experienced massive power grid failures. This led to cascading impacts, including water system disruptions and many boil water notices (BWNs). The breakdown of some communication channels and the inability to enact protective actions due to power outages, as well as travel limitations on public roads, complicated the dissemination and implementation of notifications. This research examined individuals' perceptions of risk, water quality, and BWNs during Uri. Additionally, this study sought to understand if previous experience with a BWN influenced compliance during Uri and how perceived efficacy impacted these variables. Surveying 893 Texans and Oklahomans revealed that most Uri-affected respondents believed the risks associated with BWNs were severe. Income and race were two factors that influenced BWN compliance. Age, gender, and level of education did not influence compliance. Previous experience with BWNs did not increase risk perceptions. Higher levels of perceived efficacy correlated to higher levels of compliance, perceptions of risk, and water quality, much of which support propositions of the Extended Parallel Process Model. Results suggest that pre-disaster planning and communication are imperative to helping reduce risk(s) and enhancing efficacy during a disaster, especially for novel disasters that have cascading risks, like Winter Storm Uri.


Assuntos
Planejamento em Desastres , Desastres , Comunicação em Saúde , Humanos , Sistemas Computacionais , Escolaridade , Água
7.
Nat Commun ; 15(1): 549, 2024 Jan 23.
Artigo em Inglês | MEDLINE | ID: mdl-38263406

RESUMO

Temperature is a fundamental driver of species distribution and ecosystem functioning. Yet, our knowledge of the microclimatic conditions experienced by organisms inside tropical forests remains limited. This is because ecological studies often rely on coarse-gridded temperature estimates representing the conditions at 2 m height in an open-air environment (i.e., macroclimate). In this study, we present a high-resolution pantropical estimate of near-ground (15 cm above the surface) temperatures inside forests. We quantify diurnal and seasonal variability, thus revealing both spatial and temporal microclimate patterns. We find that on average, understory near-ground temperatures are 1.6 °C cooler than the open-air temperatures. The diurnal temperature range is on average 1.7 °C lower inside the forests, in comparison to open-air conditions. More importantly, we demonstrate a substantial spatial variability in the microclimate characteristics of tropical forests. This variability is regulated by a combination of large-scale climate conditions, vegetation structure and topography, and hence could not be captured by existing macroclimate grids. Our results thus contribute to quantifying the actual thermal ranges experienced by organisms inside tropical forests and provide new insights into how these limits may be affected by climate change and ecosystem disturbances.


Assuntos
Ecossistema , Florestas , Temperatura , Mudança Climática , Sistemas Computacionais
8.
Sci Rep ; 14(1): 2020, 2024 01 23.
Artigo em Inglês | MEDLINE | ID: mdl-38263441

RESUMO

Deep neural networks (DNNs) have demonstrated higher performance results when compared to traditional approaches for implementing robust myoelectric control (MEC) systems. However, the delay induced by optimising a MEC remains a concern for real-time applications. As a result, an optimised DNN architecture based on fine-tuned hyperparameters is required. This study investigates the optimal configuration of convolutional neural network (CNN)-based MEC by proposing an effective data segmentation technique and a generalised set of hyperparameters. Firstly, two segmentation strategies (disjoint and overlap) and various segment and overlap sizes were studied to optimise segmentation parameters. Secondly, to address the challenge of optimising the hyperparameters of a DNN-based MEC system, the problem has been abstracted as an optimisation problem, and Bayesian optimisation has been used to solve it. From 20 healthy people, ten surface electromyography (sEMG) grasping movements abstracted from daily life were chosen as the target gesture set. With an ideal segment size of 200 ms and an overlap size of 80%, the results show that the overlap segmentation technique outperforms the disjoint segmentation technique (p-value < 0.05). In comparison to manual (12.76 ± 4.66), grid (0.10 ± 0.03), and random (0.12 ± 0.05) search hyperparameters optimisation strategies, the proposed optimisation technique resulted in a mean classification error rate (CER) of 0.08 ± 0.03 across all subjects. In addition, a generalised CNN architecture with an optimal set of hyperparameters is proposed. When tested separately on all individuals, the single generalised CNN architecture produced an overall CER of 0.09 ± 0.03. This study's significance lies in its contribution to the field of EMG signal processing by demonstrating the superiority of the overlap segmentation technique, optimizing CNN hyperparameters through Bayesian optimization, and offering practical insights for improving prosthetic control and human-computer interfaces.


Assuntos
Sistemas Computacionais , Gestos , Humanos , Teorema de Bayes , Eletromiografia , Redes Neurais de Computação
9.
JMIR Mhealth Uhealth ; 12: e48716, 2024 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-38180783

RESUMO

BACKGROUND: Anticoagulation management can effectively prevent complications in patients undergoing cardiac valve replacement (CVR). The emergence of eHealth tools provides new prospects for the management of long-term anticoagulants. However, there is no comprehensive summary of the application of eHealth tools in anticoagulation management after CVR. OBJECTIVE: Our objective is to clarify the current state, trends, benefits, and challenges of using eHealth tools in the anticoagulation management of patients after CVR and provide future directions and recommendations for development in this field. METHODS: This scoping review follows the 5-step framework developed by Arksey and O'Malley. We searched 5 databases such as PubMed, MEDLINE, Web of Science, CINAHL, and Embase using keywords such as "eHealth," "anticoagulation," and "valve replacement." We included papers on the practical application of eHealth tools and excluded papers describing the underlying mechanisms for developing eHealth tools. The search time ranged from the database inception to March 1, 2023. The study findings were reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Additionally, VOSviewer (version 1.6.18) was used to construct visualization maps of countries, institutions, authors, and keywords to investigate the internal relations of included literature and to explore research hotspots and frontiers. RESULTS: This study included 25 studies that fulfilled the criteria. There were 27,050 participants in total, with the sample size of the included studies ranging from 49 to 13,219. The eHealth tools mainly include computer-based support systems, electronic health records, telemedicine platforms, and mobile apps. Compared to traditional anticoagulation management, eHealth tools can improve time in therapeutic range and life satisfaction. However, there is no significant impact observed in terms of economic benefits and anticoagulation-related complications. Bibliometric analysis suggests the potential for increased collaboration and opportunities among countries and academic institutions. Italy had the widest cooperative relationships. Machine learning and artificial intelligence are the popular research directions in anticoagulation management. CONCLUSIONS: eHealth tools exhibit promise for clinical applications in anticoagulation management after CVR, with the potential to enhance postoperative rehabilitation. Further high-quality research is needed to explore the economic benefits of eHealth tools in long-term anticoagulant therapy and the potential to reduce the occurrence of adverse events.


Assuntos
Inteligência Artificial , Bibliometria , Humanos , Anticoagulantes/uso terapêutico , Sistemas Computacionais , Valvas Cardíacas
10.
PLoS One ; 19(1): e0296743, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38285674

RESUMO

In recent years, the phenomenon of the urban heat island caused by the rapid development of cities is very serious. To solve the problem of the urban heat island, this study proposed a PPP project consisting of the government (GOVT), photovoltaic investment company (PVIC), and residential customers (RS). Based on an evolutionary game model and combined with current policies and industry regulations in China, the evolution process and stable evolution strategies were studied. The result shows that more government subsidies, higher carbon trading prices, and feed-in tariffs will promote the development of the PPP project. For relatively suitable reference value ranges, the installation tilt angle of the BAPV system is 30°, the photovoltaic grid electricity price is 0.1096∼0.1296 $/kWh, the carbon trading is 8.92∼9.42 $/t.


Assuntos
Carbono , Temperatura Alta , Cidades , China , Sistemas Computacionais
11.
Sensors (Basel) ; 24(2)2024 Jan 05.
Artigo em Inglês | MEDLINE | ID: mdl-38257410

RESUMO

Detecting violent behavior in videos to ensure public safety and security poses a significant challenge. Precisely identifying and categorizing instances of violence in real-life closed-circuit television, which vary across specifications and locations, requires comprehensive understanding and processing of the sequential information embedded in these videos. This study aims to introduce a model that adeptly grasps the spatiotemporal context of videos within diverse settings and specifications of violent scenarios. We propose a method to accurately capture spatiotemporal features linked to violent behaviors using optical flow and RGB data. The approach leverages a Conv3D-based ResNet-3D model as the foundational network, capable of handling high-dimensional video data. The efficiency and accuracy of violence detection are enhanced by integrating an attention mechanism, which assigns greater weight to the most crucial frames within the RGB and optical-flow sequences during instances of violence. Our model was evaluated on the UBI-Fight, Hockey, Crowd, and Movie-Fights datasets; the proposed method outperformed existing state-of-the-art techniques, achieving area under the curve scores of 95.4, 98.1, 94.5, and 100.0 on the respective datasets. Moreover, this research not only has the potential to be applied in real-time surveillance systems but also promises to contribute to a broader spectrum of research in video analysis and understanding.


Assuntos
Fluxo Óptico , Violência , Sistemas Computacionais
12.
BMJ Open ; 14(1): e080183, 2024 01 03.
Artigo em Inglês | MEDLINE | ID: mdl-38171627

RESUMO

INTRODUCTION: While the factors commonly associated with an increased risk of child maltreatment (CM) were found to be increased during COVID-19, reports of actual maltreatment showed varying trends. Similarly, evidence regarding the impact of COVID-19 on CM within the European Cooperation on Science and Technology and Network Collaborative (COST) Action countries remains inconsistent. This scoping review aims to explore the extent and nature of evidence pertaining to CM within the countries affiliated with the Child Abuse and Neglect in Europe Action Network (Euro-CAN), funded by the COST. METHODS AND ANALYSIS: Key electronic databases were searched to identify eligible papers, reports and other material published between January 2020 and April 2023: PubMed, EMBASE, PsycINFO, Social Policy and Practice, Scopus and Web of Science. To cover the breadth of evidence, a systematic and broad search strategy was applied using a combination of keywords and controlled vocabulary for four concepts: children, maltreatment, COVID-19 and Euro-CAN countries, without restrictions on study design or language. Grey literature was searched in OpenGrey and Google Scholar. Two reviewers will independently screen full-text publications for eligibility and undertake data extraction, using a customised grid. The screening criteria and data charting will be piloted by the research team.The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) extension for scoping reviews will be followed to present the results. Results will be summarised in a tabular form and narratively. ETHICS AND DISSEMINATION: This review will identify and summarise publicly available data, without requiring ethical approval. The findings will be disseminated to the Euro-CAN Network and reported to the COST Association. They will also be published in a peer-reviewed journal. This protocol is registered on Open Science Framework.


Assuntos
COVID-19 , Maus-Tratos Infantis , Criança , Humanos , COVID-19/epidemiologia , Pandemias , Sistemas Computacionais , Europa (Continente)/epidemiologia , Projetos de Pesquisa , Revisões Sistemáticas como Assunto , Literatura de Revisão como Assunto
13.
PLoS One ; 19(1): e0296781, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38261555

RESUMO

The incorporation of information and communication technologies in the power grids has greatly enhanced efficiency in the management of demand-responses. In addition, smart grids have seen considerable minimization in energy consumption and enhancement in power supply quality. However, the transmission of control and consumption information over open public communication channels renders the transmitted messages vulnerable to numerous security and privacy violations. Although many authentication and key agreement protocols have been developed to counter these issues, the achievement of ideal security and privacy levels at optimal performance still remains an uphill task. In this paper, we leverage on Hamming distance, elliptic curve cryptography, smart cards and biometrics to develop an authentication protocol. It is formally analyzed using the Burrows-Abadi-Needham (BAN) logic, which shows strong mutual authentication and session key negotiation. Its semantic security analysis demonstrates its robustness under all the assumptions of the Dolev-Yao (DY) and Canetti- Krawczyk (CK) threat models. From the performance perspective, it is shown to incur communication, storage and computation complexities compared with other related state of the art protocols.


Assuntos
Cartões Inteligentes de Saúde , Unionidae , Animais , Biometria , Comunicação , Sistemas Computacionais , Fontes de Energia Elétrica
14.
Am J Emerg Med ; 76: 225-230, 2024 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-38128163

RESUMO

As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore the ethical and practical considerations surrounding its adoption. AI holds the potential to revolutionize how emergency physicians (EPs) make clinical decisions, but AI's complexity often surpasses EPs' capacity to provide patients with informed consent regarding its use. This article underscores the crucial need to address the ethical pitfalls of AI in EM. Patient autonomy necessitates that EPs engage in conversations with patients about whether to use AI in their evaluation and treatment. As clinical AI integration expands, this discussion should become an integral part of the informed consent process, aligning with ethical and legal requirements. The rapid availability of AI programs, fueled by vast electronic health record (EHR) datasets, has led to increased pressure on hospitals and clinicians to embrace clinical AI without comprehensive system evaluation. However, the evolving landscape of AI technology outpaces our ability to anticipate its impact on medical practice and patient care. The central question arises: Are EPs equipped with the necessary knowledge to offer well-informed consent regarding clinical AI? Collaborative efforts between EPs, bioethicists, AI researchers, and healthcare administrators are essential for the development and implementation of optimal AI practices in EM. To facilitate informed consent about AI, EPs should understand at least seven key areas: (1) how AI systems operate; (2) whether AI systems are understandable and trustworthy; (3) the limitations of and errors AI systems make; (4) how disagreements between the EP and AI are resolved; (5) whether the patient's personally identifiable information (PII) and the AI computer systems will be secure; (6) if the AI system functions reliably (has been validated); and (7) if the AI program exhibits bias. This article addresses each of these critical issues, aiming to empower EPs with the knowledge required to navigate the intersection of AI and informed consent in EM.


Assuntos
Inteligência Artificial , Medicina de Emergência , Humanos , Comunicação , Sistemas Computacionais , Consentimento Livre e Esclarecido
15.
PLoS One ; 18(12): e0293125, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38153925

RESUMO

Early evaluation and diagnosis can significantly reduce the life-threatening nature of lung diseases. Computer-aided diagnostic systems (CADs) can help radiologists make more precise diagnoses and reduce misinterpretations in lung disease diagnosis. Existing literature indicates that more research is needed to correctly classify lung diseases in the presence of multiple classes for different radiographic imaging datasets. As a result, this paper proposes RVCNet, a hybrid deep neural network framework for predicting lung diseases from an X-ray dataset of multiple classes. This framework is developed based on the ideas of three deep learning techniques: ResNet101V2, VGG19, and a basic CNN model. In the feature extraction phase of this new hybrid architecture, hyperparameter fine-tuning is used. Additional layers, such as batch normalization, dropout, and a few dense layers, are applied in the classification phase. The proposed method is applied to a dataset of COVID-19, non-COVID lung infections, viral pneumonia, and normal patients' X-ray images. The experiments take into account 2262 training and 252 testing images. Results show that with the Nadam optimizer, the proposed algorithm has an overall classification accuracy, AUC, precision, recall, and F1-score of 91.27%, 92.31%, 90.48%, 98.30%, and 94.23%, respectively. Finally, these results are compared with some recent deep-learning models. For this four-class dataset, the proposed RVCNet has a classification accuracy of 91.27%, which is better than ResNet101V2, VGG19, VGG19 over CNN, and other stand-alone models. Finally, the application of the GRAD-CAM approach clearly interprets the classification of images by the RVCNet framework.


Assuntos
COVID-19 , Redes Neurais de Computação , Humanos , Algoritmos , COVID-19/diagnóstico por imagem , Sistemas Computacionais , Hidrolases , Teste para COVID-19
16.
J Vis Exp ; (202)2023 Dec 01.
Artigo em Inglês | MEDLINE | ID: mdl-38108412

RESUMO

Advancements in cryo-electron microscopy (cryoEM) techniques over the past decade have allowed structural biologists to routinely resolve macromolecular protein complexes to near-atomic resolution. The general workflow of the entire cryoEM pipeline involves iterating between sample preparation, cryoEM grid preparation, and sample/grid screening before moving on to high-resolution data collection. Iterating between sample/grid preparation and screening is typically a major bottleneck for researchers, as every iterative experiment must optimize for sample concentration, buffer conditions, grid material, grid hole size, ice thickness, and protein particle behavior in the ice, amongst other variables. Furthermore, once these variables are satisfactorily determined, grids prepared under identical conditions vary widely in whether they are ready for data collection, so additional screening sessions prior to selecting optimal grids for high-resolution data collection are recommended. This sample/grid preparation and screening process often consumes several dozen grids and days of operator time at the microscope. Furthermore, the screening process is limited to operator/microscope availability and microscope accessibility. Here, we demonstrate how to use Leginon and Smart Leginon Autoscreen to automate the majority of cryoEM grid screening. Autoscreen combines machine learning, computer vision algorithms, and microscope-handling algorithms to remove the need for constant manual operator input. Autoscreen can autonomously load and image grids with multi-scale imaging using an automated specimen-exchange cassette system, resulting in unattended grid screening for an entire cassette. As a result, operator time for screening 12 grids may be reduced to ~10 min with Autoscreen compared to ~6 h using previous methods which are hampered by their inability to account for high variability between grids. This protocol first introduces basic Leginon setup and functionality, then demonstrates Autoscreen functionality step-by-step from the creation of a template session to the end of a 12-grid automated screening session.


Assuntos
Sistemas Computacionais , Gelo , Microscopia Crioeletrônica , Automação , Algoritmos
17.
Sensors (Basel) ; 23(24)2023 Dec 09.
Artigo em Inglês | MEDLINE | ID: mdl-38139568

RESUMO

Machine learning (ML) is a well-known subfield of artificial intelligence (AI) that aims at developing algorithms and statistical models able to empower computer systems to automatically adapt to a specific task through experience or learning from data [...].


Assuntos
Inteligência Artificial , Aprendizado de Máquina , Algoritmos , Sistemas Computacionais , Modelos Estatísticos
18.
PLoS One ; 18(12): e0295941, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38134013

RESUMO

This work analyzes the stability and performance of an offshore solar-concentrated ocean thermal energy conversion system (SC-OTEC) tied to an onshore AC grid. The OTEC is a system where electricity is generated using small temperature differences between the warm surface and deep cold ocean water. Existing control methods for SC-OTEC systems lack coordination, hindering dynamic stability and effective damping for the synchronous generator (SG). These methods struggle to quickly adapt to sudden disturbances and lack the capability to adequately reject or compensate for such disturbances due to complex control constraints and computational demands. To this regard, a control strategy combining sliding mode control (SMC) and a power system stabilizer (PSS) to improve the SC-OTEC dynamic stability and damping features for the SG. Moreover, an auxiliary secondary automatic voltage regulator is assembled with a non-linear exciter system to provide damping features. The proposed PID-PSS and secondary AVR controller gains are adaptively tuned using a modified whale optimization algorithm with the balloon effect modulation. The studied SC-OTEC is tested through MATLAB/Simulink under a severe 3ϕ short-circuit fault, solar radiation variations, and a change in surface seawater temperature as well as changes in local loads. The final findings approved that the proposed control strategy preserves a strong performance and can mimic effectively the proposed SC-OTEC damping compared to the conventional system.


Assuntos
Aeronaves , Algoritmos , Animais , Cetáceos , Sistemas Computacionais , Eletricidade , Excipientes , Água
19.
PLoS One ; 18(12): e0289162, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38117838

RESUMO

As the UAV(Unmanned Aerial Vehicle) carrying target detection algorithm in transmission line insulator inspection, we propose a lightweight YOLOv7 insulator defect detection algorithm for the problems of inferior insulator defect detection speed and high model complexity. Firstly, a lightweight DSC-SE module is designed using a DSC(Depthwise Separable Convolution) fused SE channel attention mechanism to substitute the SC(Standard Convolution) of the YOLOv7 backbone extraction network to decrease the number of parameters in the network as well as to strengthen the shallow network's ability to obtain information about target features. Then, in the feature fusion part, GSConv(Grid Sensitive Convolution) is used instead of standard convolution to further lessen the number of parameters and the computational effort of the network. EIoU-loss(Efficient-IoU) is performed in the prediction head part to make the model converge faster. According to the experimental results, the recognition accuracy rate of the improved model is 95.2%, with a model size of 7.9M. Compared with YOLOv7, the GFLOPs are reduced by 54.5%, the model size is compressed by 37.8%, and the accuracy is improved by 4.9%. The single image detection time on the Jetson Nano is 105ms and the capture rate is 13FPS. With guaranteed accuracy and detection speed, it meets the demands of real-time detection.


Assuntos
Algoritmos , Sistemas Computacionais , Reconhecimento Psicológico , Dispositivos Aéreos não Tripulados
20.
PLoS One ; 18(11): e0294858, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-38032946

RESUMO

To better study the chloride ion migration in concrete with fly ash or ground granulated blast furnace slag under low fatigue load, a Caputo time fractional-order chloride diffusion model is developed in this paper. The model, grounded in Fick's second law with a fractional-order derivative, employs an implicit numerical method for discretization, resulting in a fractional-order numerical scheme. The stability and convergence of the scheme are rigorously proven within the paper. The model's unknown parameters are estimated using genetic algorithm with a grid method. To validate the model's effectiveness, its numerical solution is juxtaposed with experimental results from chloride erosion studies. Furthermore, the fitting efficacy of the Caputo time fractional-order numerical scheme is compared with that of the classical Fick's second law numerical scheme and analytical solution. The research findings demonstrate that the fractional-order numerical scheme can more accurately simulate the chloride concentration in concrete containing fly ash or slag. Additionally, the model shows promise in predicting the service life of fly ash or slag concrete.


Assuntos
Cloretos , Cinza de Carvão , Transporte de Íons , Sistemas Computacionais , Difusão , Halogênios
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