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Comparison of incentive policies for renewable energy in an oligopolistic market with price-responsive demand
(2016) Perez De Arce Jeria, Miguel Felipe; Sauma Santis, Enzo Enrique
"This article compares different incentive policies to encourage the development of renewable energy (RE). These incentive policies (carbon tax, feed-in tariff, premium payment and quota system) are modeled in a simplified radial power network, using price-responsive demand. Most results are derived assuming an oligopolistic Cournot competitive framework and that the costs of subsidies are covered by the government (i.e., customers do not directly pay back for the subsidies). We compare the different RE incentive schemes at different congestion levels in terms of energy prices, RE generation, CO2 emissions, and social welfare.", "We find that the effectiveness of the different incentive schemes varies significantly depending on the market structure assumed, the costs of renewable energy, and the subsidy recovery method considered. Subsidy policies (FIT and premium payments) are more cost effective in reducing CO2 emissions than those policies that apply penalties or taxes, when assuming oligopoly competition and that customers do not directly pay back for the subsidies. Quota and carbon tax policies are more cost effective when assuming that either a perfectly competitive electricity market takes place or customers directly pay back for the subsidies.", "Additionally, we show that, in the feed-in tariff system, there is an interaction among incentive levels for renewable energy technologies. Given a certain feed-in tariff price to be set for a particular renewable technology, this price influences the optimal feed-in tariff price to be set for another technology."]
Ensemble Deep Learning for Wear Particle Image Analysis
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Shah R.; Sridharan N.V.; Mahanta T.K.; Muniyappa A.; Vaithiyanathan S.; Ramteke, Sangharatna M.; Marian, Max
This technical note focuses on the application of deep learning techniques in the area of lubrication technology and tribology. This paper introduces a novel approach by employing deep learning methodologies to extract features from scanning electron microscopy (SEM) images, which depict wear particles obtained through the extraction and filtration of lubricating oil from a 4-stroke petrol internal combustion engine following varied travel distances. Specifically, this work postulates that the amalgamation of ensemble deep learning, involving the combination of multiple deep learning models, leads to greater accuracy compared to individually trained techniques. To substantiate this hypothesis, a fusion of deep learning methods is implemented, featuring deep convolutional neural network (CNN) architectures including Xception, Inception V3, and MobileNet V2. Through individualized training of each model, accuracies reached 85.93% for MobileNet V2 and 93.75% for Inception V3 and Xception. The major finding of this study is the hybrid ensemble deep learning model, which displayed a superior accuracy of 98.75%. This outcome not only surpasses the performance of the singularly trained models, but also substantiates the viability of the proposed hypothesis. This technical note highlights the effectiveness of utilizing ensemble deep learning methods for extracting wear particle features from SEM images. The demonstrated achievements of the hybrid model strongly support its adoption to improve predictive analytics and gain insights into intricate wear mechanisms across various engineering applications.
Physics-Informed Machine Learning—An Emerging Trend in Tribology
(Multidisciplinary Digital Publishing Institute (MDPI), 2023) Marian, Max; Tremmel, Stephan
© 2023 by the authors.Physics-informed machine learning (PIML) has gained significant attention in various scientific fields and is now emerging in the area of tribology. By integrating physics-based knowledge into machine learning models, PIML offers a powerful tool for understanding and optimizing phenomena related to friction, wear, and lubrication. Traditional machine learning approaches often rely solely on data-driven techniques, lacking the incorporation of fundamental physics. However, PIML approaches, for example, Physics-Informed Neural Networks (PINNs), leverage the known physical laws and equations to guide the learning process, leading to more accurate, interpretable and transferable models. PIML can be applied to various tribological tasks, such as the prediction of lubrication conditions in hydrodynamic contacts or the prediction of wear or damages in tribo-technical systems. This review primarily aims to introduce and highlight some of the recent advances of employing PIML in tribological research, thus providing a foundation and inspiration for researchers and R&D engineers in the search of artificial intelligence (AI) and machine learning (ML) approaches and strategies for their respective problems and challenges. Furthermore, we consider this review to be of interest for data scientists and AI/ML experts seeking potential areas of applications for their novel and cutting-edge approaches and methods.
Boosting SpLSA for Text Classification
(2017) Hurtado, Julio; Mendoza, Marcelo; Nanculef, Ricardo
Text classification is a challenge in document labeling tasks such as spam filtering and sentiment analysis. Due to the descriptive richness of generative approaches such as probabilistic Latent Semantic Analysis (pLSA), documents are often modeled using these kind of strategies. Recently, a supervised extension of pLSA (spLSA [10]) has been proposed for human action recognition in the context of computer vision. In this paper we propose to extend spLSA to be used in text classification. We do this by introducing two extensions in spLSA: (a) Regularized spLSA, and (b) Label uncertainty in spLSA. We evaluate the proposal in spam filtering and sentiment analysis classification tasks. Experimental results show that spLSA outperforms pLSA in both tasks. In addition, our extensions favor fast convergence suggesting that the use of spLSA may reduce training time while achieving the same accuracy as more expensive methods such as sLDA or SVM.
The diminishing variance algorithm for real‐time reduction of motion artifacts in MRI
(1995) Sachs, T.S.; Meyer, C.H.; Irarrazabal, Pablo; Hu, B.S.; Nishimura, D.G.; Macovski, A.
A technique has been developed whereby motion can be detected in real time during the acquisition of data. This enables the implementation of several algorithms to reduce or eliminate motion effects from an image as it is being acquired. One such algorithm previously described is the acceptance/rejection method. This paper deals with another real‐time algorithm called the diminishing variance algorithm (DVA). With this method, a complete set of preliminary data is acquired along with information about the relative motion position of each frame of data. After all the preliminary data are acquired, the position information is used to determine which data frames are most corrupted by motion. Frames of data are then reacquired, starting with the most corrupted one. The position information is continually updated in an iterative process; therefore, each subsequent reacquisition is always done on the worst frame of data. The algorithm has been implemented on several different types of sequences. Preliminary in vivo studies indicate that motion artifacts are dramatically reduced. Copyright © 1995 Wiley‐Liss, Inc., A Wiley Company