This work presents a novel control technique for proportional power sharing among parallel VSCs connected to an islanded Microgrid in a distributed generation system consisting of Photovoltaic (PV) and Solid Oxide Fuel Cell (SOFC) as two micro-sources. For tracking the maximum solar energy, a Seeker Optimized Fuzzy based Dynamic PI (FSOA-DPI) controller is implemented for the Modified Perturb and Observe MPPT method. Again, for the optimum cost management of the Microgrid system, DPI controller based decentralized Virtual Impedance Drooping (VID) technique is implemented for suitable load sharing between the two hybrid micro-sources. An Energy Storage System (ESS) regulated by FSOA-DPI controller is also proposed for this Microgrid system to ensure the better transient and sub-transient stability during fault occurrence. The dynamic response and stability of the system with proposed method is compared and contrasted with conventional PI controller based method during load sharing for ensuring the robust control under conditions of nonlinear load and faults. The harmonic analysis has been carried out by Fast Fourier Transform (FFT) and the values indicate that the Total Harmonic Distortion (THD) is well within the prescribed IEEE standard limits. Validation and justification of the improvements achieved by the proposed controller are realized using Matlab/Simulink environment.
Keywords: Microgrid | Distributed generation (DG) | Hybrid micro-source | Photovoltaic (PV) | Solid Oxide Fuel Cell (SOFC) | Virtual Impedance Drooping | Seeker Optimization Approach (SOA)

The Earned value management (EVM) is one of the simplified analytical cost-duration assessment tools which assist project managers in monitoring the status of the project undertaken. The EVM has been elaborated by both deterministic and uncertain numbers such as fuzzy logic in the light of time. Even though cost-duration analysis is so sensitive and fluctuating in projects, the adopted approaches were unable to consider the conspicuous unreliability which is always involving the decision-making data. This problem impedes project managers to trust the foreseen inferences. To help in overcoming this critical deficiency, Z-numbers were proposed to take possibilities and reliabilities into account. Applying Z-numbers and possibilistic modeling in the EVM is a challenging topic which causes the accuracy of cost-duration tracing results to be significantly enhanced. This paper presents the application of z-numbers for modeling the earned value indicators and proves the superiority of the ZEVM against traditional fuzzy EVM. This work originally adds to the state-of-the-art literature on earned value management by presenting a proposal and applications of a new as Z-Earned Value Management (ZEVM). An illustrative case is resolved to magnify the capability of the proposed framework in dealing with higher levels of uncertainty associated with decision-making data.
Keywords: Earned value management | Fuzzy sets | Project evaluation | Uncertainty | Z-number

The Architecture, Engineering and Construction (AEC) industry is fraught with complex and difficult problems. Artificial intelligence (AI) represents a powerful tool to assist in addressing these problems. Therefore, over the years, researchers have been conducting research on AI in the AEC industry (AI-in-the-AECI). In this paper, the first comprehensive scientometric study appraising the state-of-the-art of research on AI-in-the-AECI is presented. The science mapping method was used to systematically and quantitatively analyze 41,827 related bibliographic records retrieved from Scopus. The results indicated that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC. Optimization, simulation, uncertainty, project management, and bridges have been the most commonly addressed topics/ issues using AI methods/concepts. The primary value and uniqueness of this study lies in it being the first in providing an up-to-date inclusive, big picture of the literature on AI-in-the-AECI. This study adds value to the AEC literature through visualizing and understanding trends and patterns, identifying main research interests, journals, institutions, and countries, and how these are linked within now-available studies on AI-in-the-AECI. The findings bring to light the deficiencies in the current research and provide paths for future research, where they indicated that future research opportunities lie in applying robotic automation and convolutional neural networks to AEC problems. For the world of practice, the study offers a readily-available point of reference for practitioners, policy makers, and research and development (R&D) bodies. This study therefore raises the level of awareness of AI and facilitates building the intellectual wealth of the AI area in the AEC industry.
Keywords: Architecture-engineering-construction | Artificial intelligence | Machine intelligence | Industry 4.0 | Automation | Digital transformation | Scientometric | Review

Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed arti- ficial quadratic neural networks consisting of quadratic neurons in potentially many layers. In cellular level, a quadratic function is used to replace the inner product in a traditional neuron, and then under- goes a nonlinear activation. With a single quadratic neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and quadratic counterparts can represent each other and fuzzy logic operations are naturally implemented in quadratic neural networks, it is plau- sible to explain how a deep neural network works with a quadratic network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual quadratic neu- rons, and then perform statistical/information-theoretic analyses of exemplary quadratic neural networks.
Keywords: Machine learning | Artificial neural network (ANN) | Quadratic network | Fuzzy logic

Autonomous vehicles have been envisioned to increase vehicle safety, primarily via the reduction of accidents. However, their design could also affect the vehicle travel demand and energy consumption. Although battery-powered electric and hybrid-electric autonomous vehicles assume more widespread use than conventional autonomous vehicles, energy management is harder and more significant for conventional autonomous vehicles. As such, it is necessary to investigate how to manage energy consumption in conventional autonomous vehicles. In this paper, an energy management system is constructed and analyzed by using a road-power-demand model and an intelligent system to reduce fuel consumption for a conventional autonomous vehicle. The road-power-demand model utilizes three impact factors (i) environment-conditions (ii) driver-behavior, and (iii) vehicle-specifications. The proposed intelligent energy management system includes a fuzzy-logic-system with the aim of generating the desired engine torque, based on the vehicle road power demand and a PID controller to control the air/fuel ratio, by changing the throttle angle. Results show that the intelligent energy management system reduces the vehicle energy consumption from 7.2 to 6.71 L/100 km. Next, the parameters of the fuzzy-logic-system are intelligently optimized by the particle-swarm-optimization method and new results indicate that the vehicle energy consumption is reduced by around 9.58%.
Keywords: Autonomous vehicle | Intelligent energy management | Control strategies | Conventional autonomous vehicle | Fuzzy logic system | Particle swarm optimization | Artificial Intelligence