2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)最新文献_第6页

Investigation about selective harmonic elimination in unbalanced multilevel inverters 非平衡多电平逆变器选择性谐波消除研究

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9842998

C. Buccella, M. G. Cimoroni, Carlo Cecati, A. D. Di Tommaso, C. Nevoloso, G. Schettino, S. Meo

引用次数: 0

The Decentralized Grid-Aware P2P Energy Market enabled by a Distributed Ledger Implementation 分布式账本实现的分布式电网感知P2P能源市场

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9843109

R. Jahn, Oliver Kraft, M. Klaes, Dennis Schmid, C. Rehtanz, U. Häger

引用次数: 2

Automated Route Planning of Hydrogen Deliveries 氢燃料运输的自动路线规划

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9842999

Georg M. Sehr, Mathias Vorbröcker, T. Birth, M. Scheffler, Sven Schiffner

引用次数: 0

Information extraction from clinical records: an example for breast cancer* 从临床记录中提取信息:以乳腺癌为例*

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9842995

I. Lazić, N. Jakovljević, J. Boban, I. Nosek, T. Lončar-Turukalo

{"title":"Information extraction from clinical records: an example for breast cancer*","authors":"I. Lazić, N. Jakovljević, J. Boban, I. Nosek, T. Lončar-Turukalo","doi":"10.1109/MELECON53508.2022.9842995","DOIUrl":"https://doi.org/10.1109/MELECON53508.2022.9842995","url":null,"abstract":"The extraction of relevant information from electronic health records (EHR) can facilitate large scale clinical studies related to certain diseases to uncover diversity of their biological and clinical signatures, and patterns of treatment and prognosis. Variety of EHR formats and use of clinical narrative present significant challenges to this task. In this work we describe a process of an automated information extraction from an oncology hospital clinical reports related to 2966 subjects with suspected or confirmed breast cancer. The lack of open medical term dictionaries for the Serbian language and the variety of clinical data types required, imply the use of rule-based approaches with exact matches, regular expressions, hierarchical rules and customized mini dictionaries to analyze clinical text. The accuracy of the applied approach has been validated on manually extracted clinical data of 50 breast cancer patients. The accuracy varied, field dependent, between 71.3% to 100%, indicating that certain relevant fields can be successfully captured, yet implying the need for sophisticated natural language processing tools for accurate extraction of more descriptive features.","PeriodicalId":303656,"journal":{"name":"2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126068470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Multi-spectral sensors monitoring of the epidemic of Xylella Fastidiosa in the Apulia Region 阿普利亚地区苛养木杆菌流行的多光谱传感器监测

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9843049

Sara Dell’Anna, G. Mansueto, P. Boccardo, E. Arco

{"title":"Multi-spectral sensors monitoring of the epidemic of Xylella Fastidiosa in the Apulia Region","authors":"Sara Dell’Anna, G. Mansueto, P. Boccardo, E. Arco","doi":"10.1109/MELECON53508.2022.9843049","DOIUrl":"https://doi.org/10.1109/MELECON53508.2022.9843049","url":null,"abstract":"In October 2013, the Apulia Region in Italy, unexpectedly, had to find itself to face the epidemic of Xylella Fastidiosa which in less than 10 years has decimated millions of olive trees, endangering the 40% of the regional olive-growing heritage. Taking into account the fact that there is not a definitive cure discovered yet, the only solution is the prevention linked to constant monitoring in the so-called buffer areas with diligent efforts to delimitate the phenomenon on a very local scale. Back then, remote sensing and, in particular, multispectral and hyperspectral sensors, seemed to be the key to depicting the problem to a multidimensional extent and coming up with the most adequate ways to tackle it. This research work focuses on the analysis of 4 olive groves (of the Ogliarola cultivar) in the province of Brindisi (Italy), with different symptomatic states, using a multispectral sensor mounted on a UAV system. Among a number of different vegetation indexes (IVs) calculated, the GNDVI and the BNDVI outperform to identify the effects of the Xylella Fastidiosa infection, which was further confirmed by the laboratory analyses. This confirmation has affirmed the validity of the approach used aiming at identifying “anomalies” on non-symptomatic trees and establishing a first early warning system for a subsequent more in-depth investigation in the field. Further developments will investigate the implementation of a segmentation algorithm, according to the threshold values of the IVs defined in this work, and the use of hyperspectral sensors, in order to identify anomalies on the foliage, attributable to a potential Xylella Fastidiosa infection on the trees.","PeriodicalId":303656,"journal":{"name":"2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129964458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Typical External Loads that Require to be Resisted by a Non-dominant Prosthetic Hand 需要由非优势假手抵抗的典型外部负载

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9843002

Yesenia Aquilina, M. Saliba, Jesmond Attard, A. Dingli

引用次数: 0

Coupled inductor Core design methodology to improve power-density for Interleaved buck converter with 22kW-class wireless charging of EV 提高22kw级电动汽车无线充电交错降压变换器功率密度的耦合电感磁芯设计方法

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9842913

J.C Kim, H. Lee, C.B Park, J. Lee, D. Kim, S. Ryu, S. Hong

引用次数: 0

A Model to Improve the Quality of Low-dose CT Scan Images 一种提高低剂量CT扫描图像质量的模型

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9843000

Francesca Chircop, C. J. Debono, P. Bezzina, F. Zarb

{"title":"A Model to Improve the Quality of Low-dose CT Scan Images","authors":"Francesca Chircop, C. J. Debono, P. Bezzina, F. Zarb","doi":"10.1109/MELECON53508.2022.9843000","DOIUrl":"https://doi.org/10.1109/MELECON53508.2022.9843000","url":null,"abstract":"Computed Tomography (CT) scans are used during medical imaging diagnosis as they provide detailed cross-sectional images of the human body by making use of X-rays. X-ray radiation as part of medical diagnosis poses health risks to patients leading experts to opt for low doses of radiation when possible. In accordance with European Directives, ionising radiation doses for medical purposes are to be kept as low as reasonably achievable (ALARA). While reduced radiation is beneficial from a health perspective, this impacts the quality of the images as the noise in the images increases, reducing the radiologist’s confidence in diagnosis. Various low-dose CT (LDCT) image denoising strategies available in the literature attempt to solve this conflict. However, current models face problems like over-smoothed results and lose detailed information. Consequently, the quality of LDCT images after denoising is still an important problem. The models presented in this work use deep learning techniques that are modified and trained for this problem. The results show that the best model in terms of image quality achieved a peak signal to noise ratio (PSNR) of 19.5 dB, a structural similarity index measure (SSIM) of 0.7153 and a root mean square error (RMSE) of 43.34. It performed the required operations in an average time of 4843.80s. Furthermore, tests at different dose levels were done to test the robustness of the best performing models.","PeriodicalId":303656,"journal":{"name":"2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129911637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction 基于异常检测的非对称损耗LSTM功耗预测框架

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9842895

Jihan Ghanim, Maha Issa, M. Awad

{"title":"An Asymmetric Loss with Anomaly Detection LSTM Framework for Power Consumption Prediction","authors":"Jihan Ghanim, Maha Issa, M. Awad","doi":"10.1109/MELECON53508.2022.9842895","DOIUrl":"https://doi.org/10.1109/MELECON53508.2022.9842895","url":null,"abstract":"Building an accurate load forecasting model with minimal underpredictions is vital to prevent any undesired power outages due to underproduction of electricity. However, the power consumption patterns of the residential sector contain fluctuations and anomalies making them challenging to predict. In this paper, we propose multiple Long Short-Term Memory (LSTM) frameworks with different asymmetric loss functions to impose a higher penalty on underpredictions. We also apply a density-based spatial clustering of applications with noise (DBSCAN) anomaly detection approach, prior to the load forecasting task, to remove any present oultiers. Considering the effect of weather and social factors, seasonality splitting is performed on the three considered datasets from France, Germany, and Hungary containing hourly power consumption, weather, and calendar features. Root-mean-square error (RMSE) results show that removing the anomalies efficiently reduces the underestimation and overestimation errors in all the seasonal datasets. Additionally, asymmetric loss functions and seasonality splitting effectively minimize underestimations despite increasing the overestimation error to some degree. Reducing underpredictions of electricity consumption is essential to prevent power outages that can be damaging to the community.","PeriodicalId":303656,"journal":{"name":"2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130171857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Ultra-Short-term Photovoltaic Output Power Forecasting using Deep Learning Algorithms 基于深度学习算法的超短期光伏输出功率预测

2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON) Pub Date : 2022-06-14 DOI: 10.1109/MELECON53508.2022.9843113

B. Dimd, S. Völler, O. Midtgård, Tarikua Mekashaw Zenebe

引用次数: 0