Output Linearization of Single-Input Single-Output Fuzzy System to Improve Accuracy and Performance

For fuzzy systems to be implemented effectively,the fuzzy membership function(MF)is essential.A fuzzy system(FS)that implements precise input and output MFs is presented to enhance the performance and accuracy of single-input single-output(SISO)FSs and introduce the most applicable input and output MFs protocol to linearize the fuzzy system’s output.Utilizing a variety of non-linear techniques,a SISO FS is simulated.The results of FS experiments conducted in comparable conditions are then compared.The simulated results and the results of the experimental setup agree fairly well.The findings of the suggested model demonstrate that the relative error is abated to a sufficient range(≤±10%)and that the mean absolute percentage error(MPAE)is reduced by around 66.2%.The proposed strategy to reduceMAPE using an FS improves the system’s performance and control accuracy.By using the best input and output MFs protocol,the energy and financial efficiency of every SISO FS can be improved with very little tuning of MFs.The proposed fuzzy system performed far better than other modern days approaches available in the literature.

Machine Vision Based Measurement of Dynamic Contact Angles in Microchannel Flows

When characterizing flows in miniaturized channels, the determination of the dynamic contact angle is important. By measuring the dynamic contact angle, the flow properties of the flowing liquid and the effect of material properties on the flow can be characterized. A machine vision based system to measure the contact angle of front or rear menisci of a moving liquid plug is described in this article. In this research, transparent flow channels fabricated on thermoplastic polymer and sealed with an adhesive tape are used. The transparency of the channels enables image based monitoring and measurement of flow variables, including the dynamic contact angle. It is shown that the dynamic angle can be measured from a liquid flow in a channel using the image based measurement system. An image processing algorithm has been developed in a MATLAB environment. Images are taken using a CCD camera and the channels are illuminated using a custom made ring light. Two fitting methods, a circle and two parabolas, are experimented and the results are compared in the measurement of the dynamic contact angles.

Preparation of Cyclodextrin-Based Carbonaceous Catalyst and Its Application in the Esterification

A new carbonaceous catalyst with sulfonic acid group （-SO3H） was prepared by incomplete carbonization of β-cyclodextrin followed by sulfonation.The sulfonated amorphous carbon was characterized by IR,elemental analysis,DSC-TGA and PXRD,and the catalytic activity was investigated to be an efficient catalyst for the esterification reactions with maximum yield of 87%.The sulfonated carbonaceous catalyst was readily separated from the reaction solution and keeps approximately equal catalytic activity.The results confirm that the active centre is the hydrophilic sulfonic acid functional group in the esterification reactions.

Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities

The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.

Event-Triggered Sampled-Data Consensus of Nonlinear Multi-Agent Systems with Control Input Losses

This paper investigates the leader-following consensus problem for multi-agent systems with event-triggered mechanism and control packet losses.Based on the local synchronization error, event-triggered mechanisms are proposed in order to reduce the number of controller update.The control packet may lose due to unreliability of communication channel.With the assumption that once the packet loss happens the controller will be set to zero,sufficient consensus criteria for multi-agent system with event-triggered mechanism and control packet losses is obtained.It is also shown that the interplay among the allowable packet loss rate,event-triggered mechanism and sampling period.An illustrative example is given to demonstrate the effectiveness of the theoretical results.

Distributed Deep Reinforcement Learning-based Approach for Fast Preventive Control Considering Transient Stability Constraints

Preventive transient stability control is an effective measure for the power system to withstand high-probability severe contingencies.It is mathematically an optimal power flow problem with transient stability constraints.Due to the constraints involved for differential algebraic equations of transient stability,it is difficult and time-consuming to solve this problem.To address these issues,this paper presents a novel deep reinforcement learning(DRL)framework for preventive transient stability control of power systems.A distributed deep deterministic policy gradient is utilized to train a DRL agent that can learn its control policy through massive interactions with a grid simulator.Once properly trained,the DRL agent can instantaneously provide effective strategies to adjust the system to a safe operating position with a near-optimal operational cost.The effectiveness of the proposed method is verified through numerical experiments conducted on a New England 39-bus system and NPCC 140-bus system.

Recent advances and perspectives for Zn-based batteries:Zn anode and electrolyte

Zn-based batteries have attracted extensive attention due to their high theoretical energy density,safety,abundant resources,environmental friendliness,and low cost.They are a new energy storage and conversion technology with significant development potential and have been widely used in renewable energy and portable electronic devices.Considerable attempts have been devoted to improving the performance of Zn-based batteries.Specifically,battery cycle life and energy efficiency can be improved by electrolyte modification and the construction of highly efficient rechargeable Zn anodes.This review compiles the progress of the research related to Zn anodes and electrolytes,especially in the last five years.This review will introduce fundamental concepts,summarize recent development,and inspire further systematic research for high-performance Zn-based batteries in the future.

A parameterized geometry design method for inward turning inlet compatible waverider

Intensive studies have been carried out on generations of waverider geometry and hypersonic inlet geometry. However, integration efforts of waverider and related air-intake system are restricted majorly around the X43A-like or conical flow field induced configuration, which adopts mainly the two-dimensional air-breathing technology and limits the judicious visions of developing new aerodynamic profiles for hypersonic designers. A novel design approach for integrating the inward turning inlet with the traditional parameterized waverider is proposed. The proposed method is an alternative means to produce a compatible configuration by linking the off-the-shelf results on both traditional waverider techniques and inward turning inlet techniques. A series of geometry generations and optimization solutions is proposed to enhance the lift-to-drag ratio. A quantitative but efficient aerodynamic performance evaluation approach （the hypersonic flow panel method） with lower computational cost is employed to play the role of objective function for opti- mization purpose. The produced geometry compatibility with a computational fluid dynamics （CFD） solver is also verified for detailed flow field investigation. Optimization results and other numerical validations are obtained for the feasibility demonstration of the proposed method.

Fuzzy adaptive tracking control within the full envelope for an unmanned aerial vehicle

Motivated by the autopilot of an unmanned aerial vehicle（UAV） with a wide flight envelope span experiencing large parametric variations in the presence of uncertainties, a fuzzy adaptive tracking controller（FATC） is proposed. The controller consists of a fuzzy baseline controller and an adaptive increment, and the main highlight is that the fuzzy baseline controller and adaptation laws are both based on the fuzzy multiple Lyapunov function approach, which helps to reduce the conservatism for the large envelope and guarantees satisfactory tracking performances with strong robustness simultaneously within the whole envelope. The constraint condition of the fuzzy baseline controller is provided in the form of linear matrix inequality（LMI）, and it specifies the satisfactory tracking performances in the absence of uncertainties. The adaptive increment ensures the uniformly ultimately bounded（UUB） predication errors to recover satisfactory responses in the presence of uncertainties. Simulation results show that the proposed controller helps to achieve high-accuracy tracking of airspeed and altitude desirable commands with strong robustness to uncertainties throughout the entire flight envelope.

Sliding mode controller design based on reaching law for hypersonic flight vehicle

or a hypersonic flight vehicle with highly coupling nonlinear,a sliding mode controller based on reaching law is designed for its longitudinal motion model.Two proposals of reaching law are designed.One of which is a variable exponential reaching law,the other one is compound reaching law which consists of a conventional exponential reaching law and a power rate reaching law.The reaching law controller can speed up the system states arriving at the sliding mode condition,at the same time,it can guarantee better robustness.Simulation analysis is conducted for trimmed cruise condition of 110,000 ft and Mach 15,in which the responses of the vehicle to a step change of altitude and velocity respectively are analyzed.Simulation results show that the controller based on variable exponential reaching law enables the system to faster tracking speed than the conventional reaching law.Moreover the compound reaching law controller has shorter tracking time and strong robustness against parameters uncertainties.

Structured PDMS Chambers for Enhanced Human Neuronal Cell Activity on MEA Platforms

Structured poly（dimethylsiloxane） （PDMS） chambers were designed and fabricated to enhance the signaling of human Embryonic Stem Cell （hESC） - derived neuronal networks on Microelectrode Array （MEA） platforms. The structured PDMS chambers enable cell seeding on restricted areas and thus, reduce the amount of needed coating materials and cells. In addition, the neuronal cells formed spontaneously active networks faster in the structured PDMS chambers than that in control chambers. In the PDMS chambers, the neuronal networks were more active and able to develop their signaling into organized signal trains faster than control cultures. The PDMS chamber design enables much more repeatable analysis and rapid growth of functional neuronal network in vitro. Moreover, due to its easy and cheap fabrication process, new configurations can be easily fabricated based on investigator requirements.