Scalable approach to uncertainty quantification and robust design of interconnected dynamical systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks.     

Salt Concentration Effects in Planar Light‐Emitting Electrochemical Cells

Incorporation of ions in the active layer of organic semiconductor devices may lead to attractive device properties like enhanced injection and improved carrier transport. In this paper, we investigate the effect of the salt concentration on the operation of light‐emitting electrochemical cells, using experiments and numerical calculations. The current density and light emission are shown to increase linearly with increasing ion concentration over a wide range of concentrations. The increasing current is accompanied by an ion redistribution, leading to a narrowing of the recombination zone. Hence, in absence of detrimental side reactions and doping‐related luminescence quenching, the ion concentration should be as high as possible.     

Microstructural stability and creep properties of die casting Mg–4Al–4RE magnesium alloy

The AE44 (Mg–4Al–4RE) alloy was prepared by a hot-chamber die casting method. The microstructure, microstructural stability and creep properties at 175 °C were investigated. The microstructure was analyzed by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and the Rietveld method. The results show that die cast AE44 magnesium alloy consists of α-Mg, Al₁₁RE₃, Al₂RE and Al_2.12RE_0.88 phases. The Al₁₁RE₃ phase is thermally stable at 175 °C whereas the metastable Al_2.12RE_0.88 phase undergoes a transition into the equilibrium Al₂RE phase. The alloy investigated is characterized by good creep properties at temperatures of 175 °C and 200 °C.     

Formation of mesoglobules in aqueous media from thermo-sensitive poly(ethoxytriethyleneglycol acrylate)

A series of six poly(ethoxytriethyleneglycol acrylate) (PETEGA) homopolymers were synthesized by atom transfer radical polymerization, reversible addition-fragmentation transfer polymerization, and anionic polymerization in order to cover a molecular weight range from 7,000 to 40,000 Da. The polymers exhibited a lower critical solution temperature (LCST) behavior in water, which was observed by the occurrence of a cloud point (CP) at around 35 °C. The transmittance of visible light versus temperature dependence overlapped during the cooling and the heating cycles, showing almost a complete lack of hysteresis. Moreover, instead of the occurrence of an uncontrolled macroscopic phase separation, stable colloidal aggregates (mesoglobules) of narrow distribution in particle size were formed in water at temperatures above the LCST of PETEGA at 1 g L⁻¹ solutions. The dimensions of the mesoglobules ranged from 91 to 235 nm, and particle size was not influenced by the molecular weight of PETEGA. Temperature changes caused considerable variations of the mesoglobules dimensions, which were smaller at higher temperatures. The addition of an anionic surfactant simultaneously increased the CP values by 4–6 °C and lowered the dimensions of the mesoglobules.     

Technical foundations for noninvasive assessment of changes in the width of the subarachnoid space with near-infrared transillumination-backscattering sounding (NIR-TBSS)

This paper presents technical foundations for a new technique of near-infrared transillumination-backscattering sounding, which is designed to enable noninvasive detection and monitoring of changes in the width of the subarachnoid space (SAS) and magnitude of cerebrovascular pulsation in humans. The key novelty of the technique is elimination of influence of blood flow in the scalp on the signals received from two infrared sensors-proximal and distal. A dedicated digital algorithm is used to estimate on line the ratio of the powers of received signals, referred to as two-sensor distal-to-proximal received power quotient, TQ (t). The propagation duct for NIR radiation reaching the distal sensor is the SAS filled with translucent cerebrospinal fluid. Information on slow fluctuations of the average width of the SAS is contained in the slow-variable part of the TQ (t), called the subcardiac component, and in TQ itself. Variations in frequency and magnitude of faster oscillations of the width of that space around the baseline value, dependent on cerebrovascular pulsation, are reflected in instantaneous frequency and envelope of the fast-variable component. Frequency and magnitude of the cerebrovascular pulsation depend on the action of the heart, so this fast-variable component is referred to as the cardiac component.     

ATR artificial brain project: 2004 progress report

This article presents the key assumptions and current status of the ATR Artificial Brain Project being undertaken to create Volitron, a device equipped with circuitry that enables the emergence of thought. Such thought would be recognized from Volitron's specific communication behaviors. The project consists of three complementary themes: psychodynamic architecture, brain-specific evolvable hardware, and the management of brain-building. The psychodynamic architecture is designed to develop automatically, driven by “pleasure” coming from discharges of tension gathered in special tension-accumulating devices. Tension-discharging patterns come first of all from the robot's interactions with its care giver/provider. For the dedicated hardware, we developed qcellular-automata (qCA), in which groups of uniform logic primitives (q-cells) serve as spike-train-processing units, as well as pulsed para-neural networks (PPNN) that can be evolved, using fuzzified signals and a genetic algorithm combined with hill climbing, and converted into qCA. The psychodynamic ideas were tested using three robots: Neko, equipped with a pleasure-driven associator, Miao, equipped with MemeStorms (a special working memory in which conflicting ideas fight for access to the long-term memory and actuators), and Miao+, whose brain is equipped with a growing neural network. This work was presented in part at the 9th International Symposium on Artificial Life and Robotics, Oita, Japan, January 28–30, 2004     

Pitch Detection Enhancement Employing Music Prediction

Pitch detection methods are widely used for extracting musical data from digital signals. A review of those methods is presented in the paper. Since musical signals may contain noise and distortion, detection results can be erroneous. In this paper a new method employing music prediction to support pitch determination is introduced. This method was developed in order to override disadvantages of standard pitch detection algorithms. The new approach utilizes signal segmentation and pitch prediction based on musical knowledge extraction employing artificial neural networks. Signal segmentation allows for estimating the pitch for a single note as a whole, therefore suppressing errors in transient and decay phases. Pitch prediction helps correcting pitch estimation errors by tracking musical context of the analyzed signal. As it was shown in the experimental results, pitch estimation errors may be reduced by using both signal segmentation and music prediction techniques.