Fault Tolerant Control (FTC) systems are crucial in industry to ensure safe and reliable operation, especially of motor drives. This paper proposes the use of multiple controllers for a FTC system of an induction motor drive, selected based on a switching mechanism. The system switches between sensor vector control, sensorless vector control, closed-loop voltage by frequency (V/f) control and open loop V/f control. Vector control offers high performance, while V/f is a simple, low cost strategy with high speed and satisfactory performance. The faults dealt with are speed sensor failures, stator winding open circuits, shorts and minimum voltage faults. In the event of compound faults, a protection unit halts motor operation. The faults are detected using a wavelet index. For the sensorless vector control, a novel Boosted Model Reference Adaptive System (BMRAS) to estimate the motor speed is presented, which reduces tuning time. Both simulation results and experimental results with an induction motor drive show the scheme to be a fast and effective one for fault detection, while the control methods transition smoothly and ensure the effectiveness of the FTC system. The system is also shown to be flexible, reverting rapidly back to the dominant controller if the motor returns to a healthy state.
One-third of the total waste generated in the world is construction and demolition waste. Reducing the life cycle of building materials includes increasing their recycling and reuse by using recycled aggregates. By preventing, the need to open new aggregate quarries and reducing the amount of construction waste dumped into landfills, the use of recycled concrete aggregate in drum compacted concrete protects the environment. Four samples of PRCC were prepared for testing (compressive strength, tensile strength, flexural strength, density, water absorption, porosity) as the reference mix and (10, 15, and 20%) of fine recycled concrete aggregate as a partial replacement for fine natural aggregate by volume. The mix is designed according to (ACI 327-15) with the specified cylinder compressive strength (28 MPa). The results showed a decrease in mechanical properties with an increase in partial replacement compared to the reference mixture and an increase in water absorption and porosity at 28 days. This is because old cement mortar on the surfaces of fine recycled concrete aggregates leads to higher porosity and water absorption than fine natural aggregates. At 90 days, results improved slightly. This is due to the non-aqueous cement in the recycled fine concrete aggregate.
Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.
The using of recycled aggregates from construction and demolition waste (CDW) can preserve natural aggregate resources, reduce the demand for landfill, and contribute to a sustainable built environment. Concrete demolition waste has been proven to be an excellent source of aggregates for new concrete production. At a technical, economic, and environmental level, roller compacted concrete (RCC) applications benefit various civil construction projects. Roller Compacted Concrete (RCC) is a homogenous mixture that is best described as a zero-slump concrete placed with compacting equipment, uses in storage areas, dams, and most often as a basis for rigid pavements. The mix must be sufficiently dry to support the weight of vibratory machinery while still being sufficiently moist to enough paste binder dispersion throughout the mass for efficient compaction. Limited studies into the use of RCC with fine recycled aggregate not from pavements are figured. This study aims to see how well-recycled concrete aggregates (RCA) perform in RCC mixtures. Also how well waste concrete could be used as a fine and coarse aggregate substitute in roller-compacted concrete pavement mixes, to create a good concrete mix in both wet and firm phases. The test results of mechanical properties showed 10% RCA is similar to those in the reference mix in the compressive strength, a 100% RCA ratio reduces compressive strength by almost 30%. Comparing Reference mix and Recycled concrete by 30% replacement, the compressive strength drops by just 6% when the RCA ratio is 30%.
Most existing cortico-hippocampal computational models use different artificial neural network topologies. These conventional approaches, which simulate various biological paradigms, can get slow training and inadequate conditioned responses for two reasons: increases in the number of conditioned stimuli and in the complexity of the simulated biological paradigms in different phases. In this paper, a cortico-hippocampal computational quantum (CHCQ) model is proposed for modeling intact and lesioned systems. The CHCQ model is the first computational model that uses the quantum neural networks for simulating the biological paradigms. The model consists of two entangled quantum neural networks: an adaptive single-layer feedforward quantum neural network and an autoencoder quantum neural network. The CHCQ model adaptively updates all the weights of its quantum neural networks using quantum instar, outstar, and Widrow–Hoff learning algorithms. Our model successfully simulated several biological processes and maintained the output-conditioned responses quickly and efficiently. Moreover, the results were consistent with prior biological studies.
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