The state-of-the-art determines the remaining useful lifetime (RUL) through a steady-state, fixed power cycling tests (PCT) without considering the impact of dynamically changing environmental conditions. It has resul...
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Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge ...
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Accurate prediction of the remaining useful life(RUL)is crucial for the design and management of lithium-ion *** various machine learning models offer promising predictions,one critical but often overlooked challenge is their demand for considerable run-to-failure data for *** of such training data leads to prohibitive testing efforts as the run-to-failure tests can last for ***,we propose a semi-supervised representation learning method to enhance prediction accuracy by learning from data without RUL *** approach builds on a sophisticated deep neural network that comprises an encoder and three decoder heads to extract time-dependent representation features from short-term battery operating data regardless of the existence of RUL *** approach is validated using three datasets collected from 34 batteries operating under various conditions,encompassing over 19,900 charge and discharge *** method achieves a root mean squared error(RMSE)within 25 cycles,even when only 1/50 of the training dataset is labelled,representing a reduction of 48%compared to the conventional *** also demonstrate the method's robustness with varying numbers of labelled data and different weights assigned to the three decoder *** projection of extracted features in low space reveals that our method effectively learns degradation features from unlabelled *** approach highlights the promise of utilising semi-supervised learning to reduce the data demand for reliability monitoring of energy devices.
The digital transformation process of power systems towards smart grids is resulting in improved reliability, efficiency and situational awareness at the expense of increased cybersecurity vulnerabilities. Given the a...
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The digital transformation process of power systems towards smart grids is resulting in improved reliability, efficiency and situational awareness at the expense of increased cybersecurity vulnerabilities. Given the availability of large volumes of smart grid data, machine learning-based methods are considered an effective way to improve cybersecurity posture. Despite the unquestionable merits of machine learning approaches for cybersecurity enhancement, they represent a component of the cyberattack surface that is vulnerable, in particular, to adversarial attacks. In this paper, we examine the robustness of autoencoder-based cyberattack detection systems in smart grids to adversarial attacks. A novel iterative-based method is first proposed to craft adversarial attack samples. Then, it is demonstrated that an attacker with white-box access to the autoencoder-based cyberattack detection systems can successfully craft evasive samples using the proposed method. The results indicate that naive initial adversarial seeds cannot be employed to craft successful adversarial attacks shedding insight on the complexity of designing adversarial attacks against autoencoder-based cyberattack detection systems in smart grids.
The impact of orthopedic scaffolds on bone defect healing,particularly the late-stage bone remodeling process,is pivotal for the therapeutic *** study applies fadditively manufactured scaffolds composed of hydroxyapat...
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The impact of orthopedic scaffolds on bone defect healing,particularly the late-stage bone remodeling process,is pivotal for the therapeutic *** study applies fadditively manufactured scaffolds composed of hydroxyapatite-doped poly(lactide-co-glycolide)-b-poly(ethylene glycol)-b-poly(lactide-co-glycolide)(HAPELGA)with varying properties to treat rat calvarial defects,elucidating their significant role in bone remodeling by modulating physiological *** engineered two scaffolds with different polylactic acid(PLA)to polyglycolic acid(PGA)ratio(9/1 and 18/1)to vary in hydrophobicity,degradation rate,mechanical properties,and structural *** variations influenced physiological responses,including osteogenesis,angiogen-esis,and immune reactions,thereby guiding bone *** findings show that the HA-PELGA(18/1)scaffold,with a slower degradation rate,supported bulk bone formation due to a stable ***,the HA-PELGA(9/1)scaffold,with a faster degradation rate and more active interfaces,facilitated the formation of a thin bone layer and higher bone *** study demonstrates these degradable scaffolds help to promote bone healing and reveals how scaffold properties influence the bone remodeling process,offering a potential strategy to optimize scaffold design aiming at late-stage bone defect healing.
Deep learning has been proved to diagnose Attention Deficit/Hyperactivity Disorder (ADHD) accurately, but it has raised concerns about trustworthiness because of the lack of explainability. Fortunately, the developmen...
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This study presents a performance comparison of different deep machine learning models for the intelligent segregation of date fruit bunches of numerous varieties. The shape, size, and color of the various types of da...
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Congenital heart diseases (CHDs), caused by structural abnormalities in the heart and blood vessels, pose a significant public health concern and contribute significantly to the socioeconomic burden, particularly in p...
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Wildlife conservation is a pressing global concern, with illegal poaching posing a severe threat to many endangered species. In recent years, advanced technologies like machine learning and digital signal processing h...
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This paper proposes a 1D residual convolutional neural network (CNN) for classifying arrhythmias based on electrocardiogram (ECG) signals. The additional residual blocks and skip connections effectively alleviate the ...
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Urinalysis is one of the simplest and most common medical tests in modern cities. With the assistance of professional technicians and equipment, people in metropolitan areas can effortlessly acquire information about ...
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