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Grid-connected Converters with Virtual Electromechanical Characteristics:Experimental Verification

CSEE Journal of Power and Energy Systems 2017年第3期3卷 286-295页

作者： Weiyi Zhang Daniel Remon Ignacio Candela alvaro luna Pedro Rodriguez Electrical Engineering Department Technical University of CataloniaBarcelona 08222Spain Department of Engineering University Loyola Andaluc´ıaSeville 41014Spain

Grid-connected power converters,which are frequently used to link renewable generation plants with the grid,are required to provide a better functionality for large scale integration of renewables.They are expected to be gridfriendly,or even grid-supportive,instead of simply grid-feeding or grid-demanding.This paper designs a synchronous power controller for grid-connected converters in detail,emulating the electromechanical characteristics of synchronous machines and improving even its actual performance,as it is based on a virtual approach.Based on this design,the grid-interfacing units are capable of showing inertia,damping,and droop characteristics as synchronous machines and presenting thus a grid-supporting behavior.The detailed control design and experimental validation on a 10 kW laboratory setup acts as the main contribution of this paper,compared with the existing studies on generator emulation controls.

关键词： DC-AC power conversion power generation control synchronous power controller virtual electromechanical characteristics

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Clinical impact of artificial intelligence-based solutions on imaging of the pancreas and liver

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World Journal of Gastroenterology 2023年第9期29卷 1427-1445页

作者： M alvaro Berbís Felix Paulano Godino Javier Royuela del Val Lidia AlcaláMata Antonio luna Department of Radiology HT MédicaSan Juan de Dios HospitalCórdoba 14960Spain Faculty of Medicine Autonomous University of MadridMadrid 28049Spain Department of Radiology HT MédicaClínica las NievesJaén 23007Spain

Artificial intelligence(AI)has experienced substantial progress over the last ten years in many fields of application,including healthcare.In hepatology and pancreatology,major attention to date has been paid to its application to the assisted or even automated interpretation of radiological images,where AI can generate accurate and reproducible imaging diagnosis,reducing the physicians’workload.AI can provide automatic or semi-automatic segmentation and registration of the liver and pancreatic glands and lesions.Furthermore,using radiomics,AI can introduce new quantitative information which is not visible to the human eye to radiological reports.AI has been applied in the detection and characterization of focal lesions and diffuse diseases of the liver and pancreas,such as neoplasms,chronic hepatic disease,or acute or chronic pancreatitis,among others.These solutions have been applied to different imaging techniques commonly used to diagnose liver and pancreatic diseases,such as ultrasound,endoscopic ultrasonography,computerized tomography(CT),magnetic resonance imaging,and positron emission tomography/CT.However,AI is also applied in this context to many other relevant steps involved in a comprehensive clinical scenario to manage a gastroenterological patient.AI can also be applied to choose the most convenient test prescription,to improve image quality or accelerate its acquisition,and to predict patient prognosis and treatment response.In this review,we summarize the current evidence on the application of AI to hepatic and pancreatic radiology,not only in regard to the interpretation of images,but also to all the steps involved in the radiological workflow in a broader sense.Lastly,we discuss the challenges and future directions of the clinical application of AI methods.

关键词： Artificial intelligence Machine learning Deep learning Imaging Liver Pancreas

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Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

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World Journal of Gastroenterology 2021年第27期27卷 4395-4412页

作者： M alvaro Berbís José Aneiros-Fernández F Javier Mendoza Olivares Enrique Nava Antonio luna Department of R&D HT MédicaMadrid 28046MadridSpain Department of Pathology Hospital Universitario Clínico San CecilioGranada 18012Spain Department of Gastroenterology Fatima ClinicSevilla 41012Spain Department of Communications Engineering University of MálagaMalaga 29016Spain MRI Unit Department of RadiologyHT MédicaJaén 23007Spain

The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging,improve the interpretability of results,and support decision-making for the detection and prevention of diseases.Radiology,endoscopy and pathology images are suitable for deep-learning analysis,potentially changing the way care is delivered in gastroenterology.The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions,by discussing how different models behave in critical tasks,such as lesion detection or characterization(i.***.the distinction between benign and malignant lesions of the esophagus,the stomach and the colon).To this end,we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology,endoscopy and histologic whole-slide images of the gastrointestinal tract.

关键词： Artificial intelligence Machine learning Radiology Endoscopy Pathology Gastroenterology

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