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New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN) exhibit excellent performance in complex problems and have been increasingly applied in the research field of construction management (CM) over the last few decades. However, few papers draw up a systematic review to evaluate the state-of-the-art research on ANN in CM. In this paper, content analysis is performed to comprehensively analyze 112 related bibliographic records retrieved from seven selected top journals published between 2000 and 2020. The results indicate that the applications of ANN of interest in CM research have been significantly increasing since 2015. Back-propagation was the most widely used algorithm in training ANN. Integrated ANN with fuzzy logic/genetic algorithm was the most commonly employed way of addressing the CM problem. In addition, 11 application fields and 31 research topics were identified, with the primary research interests focusing on cost, performance, and safety. Lastly, challenges and future directions for ANN in CM were put forward from four main areas of input data, modeling, application fields, and emerging technologies. This paper provides a comprehensive understanding of the application of ANN in CM research and useful reference for the future. [ABSTRACT FROM AUTHOR]

Medical image semantic segmentation is essential in computer-aided diagnosis systems. It can separate tissues and lesions in the image and provide valuable information to radiologists and doctors. The breast ultrasound (BUS) images have advantages: no radiation, low cost, portable, etc. However, there are two unfavorable characteristics: (1) the dataset size is often small due to the difficulty in obtaining the ground truths, and (2) BUS images are usually in poor quality. Trustworthy BUS image segmentation is urgent in breast cancer computer-aided diagnosis systems, especially for fully understanding the BUS images and segmenting the breast anatomy, which supports breast cancer risk assessment. The main challenge for this task is uncertainty in both pixels and channels of the BUS images. In this paper, we propose a Spatial and Channel-wise Fuzzy Uncertainty Reduction Network (SCFURNet) for BUS image semantic segmentation. The proposed architecture can reduce the uncertainty in the original segmentation frameworks. We apply the proposed method to four datasets: (1) a five-category BUS image dataset with 325 images, and (2) three BUS image datasets with only tumor category (1830 images in total). The proposed approach compares state-of-the-art methods such as U-Net with VGG-16, ResNet-50/ResNet-101, Deeplab, FCN-8s, PSPNet, U-Net with information extension, attention U-Net, and U-Net with the self-attention mechanism. It achieves 2.03%, 1.84%, and 2.88% improvements in the Jaccard index on three public BUS datasets, and 6.72% improvement in the tumor category and 4.32% improvement in the overall performance on the five-category dataset compared with that of the original U-shape network with ResNet-101 since it can handle the uncertainty effectively and efficiently. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm to combine two or more techniques of maximum power point tracking (MPPT) to increase the output dc power and the efficiency of photovoltaic (PV) arrays. Different MPPT methods have dissimilar responses for the same environmental circumstances. The combination of multiple methods has the advantage of ever acquiring maximum power as the environmental conditions change. The proposed algorithm is used to enhance the selection of the most suitable duty cycle of the combined methods, based on the power-voltage characteristics, to get the best tracking response. Multiple classical and/or artificial intelligence (AI)-based MPPT methods can be combined based on this selection algorithm. To demonstrate its effectiveness, two examples are illustrated. The first one is the combination of two classical MPPT methods, which are the incremental conductance (INC) and the perturb-and-observe (P&O). The second example is to combine two AI-based MPPT, which are the artificial neural network (ANN) and the fuzzy logic control (FLC). The simulation results of the application of the proposed algorithm to a grid-connected PV system model justify its capability to acquire better static and dynamic responses than the responses of individual MPPT methods. [ABSTRACT FROM AUTHOR]

Classic and intelligent techniques aim to locate and track the maximum power point of photovoltaic (PV) systems, such as perturb and observe (P&O), fuzzy logic (FL), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs). This paper proposes and compares three intelligent algorithms for maximum power point tracking (MPPT) control, specifically fuzzy, ANN, and ANFIS. The modeling of a single-diode equivalent circuit-based 3 kWp PV plant was developed and validated to achieve this purpose. Then, the MPPT techniques were designed and applied to control the buck–boost converter's switching device of the PV plant. All three methods use the ambient conditions as input variables: solar irradiance and ambient temperature. The proposed methodology comprises the study of the dynamic response for tracking the maximum power point and the power generated of the PV systems, and it was compared to the classic P&O technique under varying ambient conditions. We observed that the intelligent techniques outperformed the classic P&O method in tracking speed, tracking accuracy, and reducing oscillation around the maximum power point (MPP). The ANN technique was the better control algorithm in energy gain, managing to recover up to 9.9% power. [ABSTRACT FROM AUTHOR]

This article studies the problem of adaptive fuzzy fixed-time decentralized control for nonlinear interconnected systems with stochastic disturbances and unknown nonlinearities. In control design, fuzzy logic systems are utilized to identify the unknown nonlinear functions. Combining stochastic fixed-time Lyapunov stability theory with adaptive backstepping control technique, a fuzzy adaptive fixed-time decentralized control design algorithm is developed by adopting smooth projection operator. It is proved that all the state variables of the closed-loop system are globally fixed-time stable in probability. Finally, both numerical and practical examples are provided to elucidate the feasibility of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Artificial intelligence-based methodology [artificial neural network (ANN) and fuzzy logic] and a multiple regression-based analysis were conducted for modeling of the biogas production rate from a real full-scale sludge digestion process. In the computational analysis, five process-related parameters such as influent sludge flow rate, total solids content, total volatile solids content, alkalinity, and volatile fatty acids concentration were considered as the input variables for the proposed models. In the ANN-based modeling, a benchmark comparison of 11 backpropagation (BP) algorithms was employed in the first step, and the scaled conjugate gradient algorithm was chosen as the best BP algorithm in terms of their respective mean squared errors. According to the selected BP algorithm (scaled conjugate gradient BP), the number of neurons at the hidden layer was optimized as 14, and the coefficient of determination (R2) was obtained as 0.65 for the optimal three-layer ANN structure (5:14:1). In the second part of the study, a MISO (multiple-input single-output)-type fuzzy-logic model was developed, and five input variables were fuzzified in a knowledge-based manner. For the fuzzy subsets, trapezoidal membership functions were implemented with 10 and 20 levels, and a Mamdani-type fuzzy inference system (FIS) was used to employ 394 rules in the if–then form. The product, summation, and centroid methods were employed, respectively, for implication, aggregation, and defuzzification processes conducted in the FIS. Fuzzy logic-produced forecasts were compared with the estimations obtained from both ANN-based model and a polynomial multiple regression-based model (employed as the third part of this study) derived in this study. Findings of this study clearly indicated that compared to ANN model and conventional multiple regression approach, the proposed MISO fuzzy logic-based model produced smaller deviations and exhibited a superior predictive performance on forecasting biogas production rate from a full-scale treatment plant with a satisfactory R2 value of 0.88. [ABSTRACT FROM AUTHOR]