The rapid development of digital technologies and solutions is disrupting the energy sector. In this regard, digitalization is a facilitator and enabler for integrating renewable energies, management and operation. Among these, advanced monitoring techniques and artificial intelligence may be applied in solar PV plants to improve their operation and efficiency and detect potential malfunctions at an early stage. This paper proposes a Digital Twin DT concept, mainly focused on O&M, to obtain more information about the system by using several artificial intelligence boxes. Furthermore, it includes the development of several machine learning (ML) algorithms capable of reproducing the expected behavior of the solar PV plant and detecting the malfunctioning of different components. In this regard, this allows for reducing downtime and optimizing asset management. In this paper, different ML techniques are used and compared to optimize the selected methods for enhanced response. The paper presents all stages of the developed Digital Twin, including ML model development with an accuracy of 98.3% of the whole DT, and finally, a communication and visualization platform. The different responses and comparisons have been made using a model based on MATLAB/Simulink using different cases and system conditions.

The practice of detecting skin cancer is based primarily on a visual examination by a dermatologist, followed by a series of tests for a more accurate diagnosis. The concept “the earlier cancer is detected in its natural history, the more effective the treatment is likely to be" is also valid for skin cancer. Hence, any delayed or missed diagnosis can lead to a more severe clinical stage or, what's worse, death. On the other hand, the lack of biomarkers in clinical use brings about overdiagnosis and unnecessary biopsies. DL-CAD system seems to be an excellent candidate for improving diagnostic accuracy and reducing unnecessary treatments. However, the vast majority of conventional CADs manipulate dermoscopic images, which require not only costly equipment but also time-consuming processing. Despite the difficulties with precision, state-of-the-art DL-CAD systems provide an interpretation using digital images, requiring no expertise in cost-effective dermoscopic image capture and interpretation. Pre-processing methods play a crucial role in solving this problem. This study presents results with regard to pre-processing steps to improve the images to be used in the diagnosis of the 5 most common skin cancer types for the proposed CNN based ResNet50 deep learning model. To the best of our knowledge it is the first time that ResNet50 deep-learning model has been utilized in diagnosis of skin cancer. Cilt kanserini tespit edilmesi öncelikle bir dermatolog tarafından yapılan görsel muayeneye ve ardından daha doğru bir tanı için bir dizi teste dayanmaktadır. "Kanser doğal geçmişinde ne kadar erken tespit edilirse, tedavinin o kadar etkili olması muhtemeldir" kavramı cilt kanseri için de geçerlidir. Bu nedenle, gecikmiş veya kaçırılmış herhangi bir tanı daha ağır bir klinik aşamaya veya daha da kötüsü ölüme yol açabilir. Öte yandan, klinik kullanımda biyomarker eksikliği aşırı tanı ve gereksiz biyopsileri beraberinde getirmektedir. DL-CAD sistemleri tanısal doğruluğu artırmak ve gereksiz tedavileri azaltmak için mükemmel bir aday gibi görünmektedir. Bununla birlikte, geleneksel CAD sistemlerin büyük çoğunluğu, yüksek maliyetli ekipmanın yansıra işlenmesi zaman alan dermoskopik görüntüleri kullanır. Hassasiyet hususundaki zorluklara rağmen, modern DL-CAD sistemleri, dijital görüntüleri kullanarak bir yorumlama sağlar ve uygun maliyetli dermoskopik görüntü yakalama ve yorumlamada uzmanlık gerektirmez. Ön işleme yöntemleri bu sorunun çözümünde çok önemli bir rol oynamaktadır. Bu çalışma, önerilen CNN tabanlı ResNet50 derin öğrenme modeli için en yaygın 5 cilt kanseri türünün teşhisinde kullanılacak görüntülerin iyileştirilmesine yönelik ön işleme adımlarına ilişkin sonuçları sunmaktadır. Bildiğimiz kadarıyla, cilt kanseri tanısında ResNet50 derin öğrenme modeli ilk kez kullanılmıştır.

This study explores virtual sensing techniques for the Eolink floating offshore wind turbine (FOWT), which features a pyramidal platform and a single-point mooring system that enables weathervaning to maximize power production and reduce structural loads. To address the challenges and costs associated with monitoring submerged components, virtual sensors are investigated as an alternative to physical instrumentation. The main objective is to design a virtual sensor of mooring hawser loads using a reduced set of input features from GPS, anemometer, and inertial measurement unit (IMU) data. A virtual sensor is also proposed to estimate the bending moment at the joint of the pyramid masts. The FOWT is modeled in OrcaFlex, and a range of load cases is simulated for training and testing. Under defined sensor sampling conditions, both supervised and physics-informed machine learning algorithms are evaluated. The models are tested under aligned and misaligned environmental conditions, as well as across operating regimes below- and above-rated conditions. Results show that mooring tensions can be estimated with high accuracy, while bending moment predictions also perform well, though with lower precision. These findings support the use of virtual sensing to reduce instrumentation requirements in critical areas of the floating wind platform.