Modification of rheological properties of a thermotropic liquid crystalline polymer by melt-state reactive processing

Thermotropic main-chain liquid crystalline polymers typically have very low melt viscosity with strong temperature dependence compared to other common thermoplastics. While this is beneficial in some processing applications, such as injection molding, it presents challenges for others, such as coextrusion. In this study, the rheological properties of a thermotropic main-chain liquid crystalline polymer (Vectra A950) were enhanced by melt-state reactive processing with triphenyl phosphite (TPP), which can react with up to three polymer chain-ends through their chain-end functionalities. The influence of processing time and TPP content on the shear viscosity and other important material properties were investigated. Optimal conditions, which increased the shear viscosity by nearly a factor of 20 over the neat polymer, were found to be 4 wt% TPP and 30 min of reaction time at 290 °C. Further results from differential scanning calorimetry, wide-angle X-ray diffraction and polarized optical microscopy confirmed that coupling with TPP did not affect the microstructure, melting/crystallization behavior or liquid crystallinity. The stability of TPP-modified samples was also studied at 80 °C in air and following melt reprocessing at 290–300 °C under N₂ or air. Samples were stable (as measured by shear viscosity) for more than one month at 80 °C in air or when reprocessed in N₂ at 290 °C for up to 10 min. However, when reprocessed at 300 °C in air, the viscosity enhancement was partially reversed due to scission of P–O bonds that were formed during the initial reaction between the polymer chain-ends and TPP.     

Some interval-valued q-rung orthopair weighted averaging operators and their applications to multiple-attribute decision making

Q-rung orthopair fuzzy sets (q-ROFSs), initially proposed by Yager, are a new way to reflect uncertain information. The existing intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets are special cases of the q-ROFSs. However, due to insufficiency in available information, it is difficult for decision makers to exactly express the membership and nonmembership degrees by crisp numbers, and interval membership degree and interval nonmembership degree are good choices. In this paper, we propose the concept of interval-valued q-rung orthopair fuzzy set (IVq-ROFS) based on the ideas of q-ROFSs and some operational laws of q-rung orthopair fuzzy numbers (q-ROFNs). Then, some interval-valued q-rung orthopair weighted averaging operators are presented based on the given operational laws of q-ROFNs. Further, based on these operators, we develop a novel approach to solve multiple-attribute decision making (MADM) problems under interval-valued q-rung orthopair fuzzy environment. Finally, a numerical example is provided to illustrate the application of the proposed method, and the sensitivity analysis is further carried out for the parameters.     

Including Joint Torques and Power Consumption in the Stability Margin of Walking Robots

Walking robots possess important inherent advantages as autonomous systems, and many techniques have been developed during the last three decades to improve these mobile systems significantly. However, when robots attempt to walk through realistic scenarios, some techniques exhibit important shortcomings. One such shortcoming is to define the robot's quasi-static-stability margin using only the geometric parameters of the robot, neglecting the influence of real systems' motor-torque and power-consumption limitations. This paper reviews quasi-static stability theory for walking robots, illustrates real problems through simulation and experiments using real walking machines, and proposes a new concept of quasi-static stability that takes into consideration some of the robot's intrinsic parameters. The resulting stability measurement can improve efficiency in terms of robot design and power consumption, two aspects that are of paramount importance in autonomous walking robots for real applications.     

Robust multidisciplinary UAS design optimisation

There are many applications in aeronautical/aerospace engineering where some values of the design parameters/states cannot be provided or determined accurately. These values can be related to the geometry (wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi-Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the first case considers robust multi-objective (single-disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero-structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design.     

Real-time massive convolution for audio applications on GPU

Massive convolution is the basic operation in multichannel acoustic signal processing. This field has experienced a major development in recent years. One reason for this has been the increase in the number of sound sources used in playback applications available to users. Another reason is the growing need to incorporate new effects and to improve the hearing experience. Massive convolution requires high computing capacity. GPUs offer the possibility of parallelizing these operations. This allows us to obtain the processing result in much shorter time and to free up CPU resources. One important aspect lies in the possibility of overlapping the transfer of data from CPU to GPU and vice versa with the computation, in order to carry out real-time applications. Thus, a synthesis of 3D sound scenes could be achieved with only a peer-to-peer music streaming environment using a simple GPU in your computer, while the CPU in the computer is being used for other tasks. Nowadays, these effects are obtained in theaters or funfairs at a very high cost, requiring a large quantity of resources. Thus, our work focuses on two mains points: to describe an efficient massive convolution implementation and to incorporate this task to real-time multichannel-sound applications.     

Online global learning in direct fuzzy controllers

A novel approach to achieve real-time global learning in fuzzy controllers is proposed. Both the rule consequents and the membership functions defined in the premises of the fuzzy rules are tuned using a one-step algorithm, which is capable of controlling nonlinear plants with no prior offline training. Direct control is achieved by means of two auxiliary systems: The first one is responsible for adapting the consequents of the main controller's rules to minimize the error arising at the plant output, while the second auxiliary system compiles real input-output data obtained from the plant. The system then learns in real time from these data taking into account, not the current state of the plant but rather the global identification performed. Simulation results show that this approach leads to an enhanced control policy thanks to the global learning performed, avoiding overfitting.     

Predictor-based stabilization of discrete time-varying input-delay systems

This paper adapts some literature results on stabilizing discrete time-varying input delay systems to the case of having a predictor-based controller. The objective of the paper is showing that the incorporation of predictors to the referred results is relatively simple and, by means of such predictors, robustness against delay mismatch in the input channel may be improved. In this way, larger delay variation margins are proved with predictors when compared to standard memoryless state feedback, as intuitively expected, due to the extra past information available.     

DYLEMA: Using walking robots for landmine detection and location

Detection and removal of antipersonnel landmines is an important worldwide concern. A huge number of landmines has been deployed over the last twenty years, and demining will take several more decades, even if no more mines were deployed in future. An adequate mine-clearance rate can only be achieved by using new technologies such as improved sensors, efficient manipulators and mobile robots. This paper presents some basic ideas on the configuration of a mobile system for detecting and locating antipersonnel landmines efficiently and effectively. The paper describes the main features of the overall system, which consists of a sensor head that can detect certain landmine types, a manipulator to move the sensor head over large areas, a locating system based on a global-positioning system, a remote supervisor computer and a legged robot used as the subsystems’ carrier. The whole system has been configured to work in a semi-autonomous mode with a view also to robot mobility and energy efficiency.