Switched Linear Model Predictive Controllers for Periodic Exogenous Signals

This paper develops linear switched controllers for periodic exogenous signals using the framework of a continuous-time model predictive control. In this framework, the control signal is generated by an algorithm that uses receding horizon control principle with an on-line optimization scheme that permits inclusion of operational constraints. Unlike traditional repetitive controllers, applying this method in the form of switched linear controllers ensures rumpless transfer from one controller to another. Simulation studies are included to demonstrate the efficacy of the design with or without hard constraints

Long-Term Human Video Generation of Multiple Futures Using Poses

Predicting future human behavior from an input human video is a useful task for applications such as autonomous driving and robotics. While most previous works predict a single future, multiple futures with different behavior can potentially occur. Moreover, if the predicted future is too short (e.g., less than one second), it may not be fully usable by a human or other systems. In this paper, we propose a novel method for future human pose prediction capable of predicting multiple long-term futures. This makes the predictions more suitable for real applications. Also, from the input video and the predicted human behavior, we generate future videos. First, from an input human video, we generate sequences of future human poses (i.e., the image coordinates of their body-joints) via adversarial learning. Adversarial learning suffers from mode collapse, which makes it difficult to generate a variety of multiple poses. We solve this problem by utilizing two additional inputs to the generator to make the outputs diverse, namely, a latent code (to reflect various behaviors) and an attraction point (to reflect various trajectories). In addition, we generate long-term future human poses using a novel approach based on unidimensional convolutional neural networks. Last, we generate an output video based on the generated poses for visualization. We evaluate the generated future poses and videos using three criteria (i.e., realism, diversity and accuracy), and show that our proposed method outperforms other state-of-the-art works

Enabling Thin and Flexible Solid-State Composite Electrolytes by the Scalable Solution Process

All solid-state batteries (ASSBs) have the potential to deliver higher energy densities, wider operating temperature range, and improved safety compared with today's liquid-electrolyte-based batteries. However, of the various solid-state electrolyte (SSE) classes - polymers, sulfides, or oxides - none alone can deliver the combined properties of ionic conductivity, mechanical, and chemical stability needed to address scalability and commercialization challenges. While promising strategies to overcome these include the use of polymer/oxide or sulfide composites, there is still a lack of fundamental understanding between different SSE-polymer-solvent systems and its selection criteria. Here, we isolate various SSE-polymer-solvent systems and study their molecular level interactions by combining various characterization tools. With these findings, we introduce a suitable Li7P3S11SSE-SEBS polymer-xylene solvent combination that significantly reduces SSE thickness (∼50 μm). The SSE-polymer composite displays high room temperature conductivity (0.7 mS cm-1) and good stability with lithium metal by plating and stripping over 2000 h at 1.1 mAh cm-2. This study suggests the importance of understanding fundamental SSE-polymer-solvent interactions and provides a design strategy for scalable production of ASSBs

MM Algorithms for Geometric and Signomial Programming

This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates.Comment: 16 pages, 1 figur

A general framework for learning prosodic-enhanced representation of rap lyrics

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Learning and analyzing rap lyrics is a significant basis for many Web applications, such as music recommendation, automatic music categorization, and music information retrieval, due to the abundant source of digital music in the World Wide Web. Although numerous studies have explored the topic, knowledge in this field is far from satisfactory, because critical issues, such as prosodic information and its effective representation, as well as appropriate integration of various features, are usually ignored. In this paper, we propose a hierarchical attention variational a utoe ncoder framework (HAVAE), which simultaneously considers semantic and prosodic features for rap lyrics representation learning. Specifically, the representation of the prosodic features is encoded by phonetic transcriptions with a novel and effective strategy (i.e., rhyme2vec). Moreover, a feature aggregation strategy is proposed to appropriately integrate various features and generate prosodic-enhanced representation. A comprehensive empirical evaluation demonstrates that the proposed framework outperforms the state-of-the-art approaches under various metrics in different rap lyrics learning tasks

Pseudo-Killing Spinors, Pseudo-supersymmetric p-branes, Bubbling and Less-bubbling AdS Spaces

We consider Einstein gravity coupled to an n-form field strength in D dimensions. Such a theory cannot be supersymmetrized in general, we nevertheless propose a pseudo-Killing spinor equation and show that the AdS X Sphere vacua have the maximum number of pseudo-Killing spinors, and hence are fully pseudo-supersymmetric. We show that extremal p-branes and their intersecting configurations preserve fractions of the pseudo-supersymmetry. We study the integrability condition for general (D,n) and obtain the additional constraints that are required so that the existence of the pseudo-Killing spinors implies the Einstein equations of motion. We obtain new pseudo-supersymmetric bubbling AdS_5 X S^5 spaces that are supported by a non-self-dual 5-form. This demonstrates that non-supersymmegtric conformal field theories may also have bubbling states of arbitrary droplets of free fermions in the phase space. We also obtain an example of less-bubbling AdS geometry in D=8, whose bubbling effects are severely restricted by the additional constraint arising from the integrability condition.Comment: typos corrected, extra comments and references added, version appeared in JHE

Open-loop position control in collaborative, modular Variable-Stiffness-Link (VSL) robots

— Collaborative robots (cobots) open up new avenues in the fields of industrial robotics and physical Human-Robot Interaction (pHRI) as they are suitable to work in close approximation and in collaboration with humans. The integration and control of variable stiffness elements allow inherently safe interaction. Apart from notable work on Variable Stiffness Actuators, the concept of Variable-Stiffness-Link (VSL) manipulators promises safety improvements in cases of unintentional physical collisions. However, position control of these type of robotic manipulators is challenging for critical task-oriented motions (e.g., pick and place). Hence, the study of open-loop position control for VSL robots is crucial to achieve high levels of safety, accuracy and hardware cost-efficiency in pHRI applications. In this paper, we propose a hybrid, learning based kinematic modelling approach to improve the performance of traditional open-loop position controllers for a modular, collaborative VSL robot. We show that our approach improves the performance of traditional open-loop position controllers for robots with VSL and compensates for position errors, in particular, for lower stiffness values inside the links: Using our upgraded and modular robot, two experiments have been carried out to evaluate the behaviour of the robot during taskoriented motions. Results show that traditional model-based kinematics are not able to accurately control the position of the end-effector: the position error increases with higher loads and lower pressures inside the VSLs. On the other hand, we demonstrate that, using our approach, the VSL robot can outperform the position control compared to a robotic manipulator with 3D printed rigid links

In situ evidence for the structure of the magnetic null in a 3D reconnection event in the Earth's magnetotail

Magnetic reconnection is one of the most important processes in astrophysical, space and laboratory plasmas. Identifying the structure around the point at which the magnetic field lines break and subsequently reform, known as the magnetic null point, is crucial to improving our understanding reconnection. But owing to the inherently three-dimensional nature of this process, magnetic nulls are only detectable through measurements obtained simultaneously from at least four points in space. Using data collected by the four spacecraft of the Cluster constellation as they traversed a diffusion region in the Earth's magnetotail on 15 September, 2001, we report here the first in situ evidence for the structure of an isolated magnetic null. The results indicate that it has a positive-spiral structure whose spatial extent is of the same order as the local ion inertial length scale, suggesting that the Hall effect could play an important role in 3D reconnection dynamics.Comment: 14 pages, 4 figure

Synchronous nanoscale topographic and chemical mapping by differential-confocal controlled Raman microscopy

Confocal Raman microscopy is currently used for label-free optical sensing and imaging within the biological, engineering, and physical sciences as well as in industry. However, currently these methods have limitations, including their low spatial resolution and poor focus stability, that restrict the breadth of new applications. This paper now introduces differential-confocal controlled Raman microscopy as a technique that fuses differential confocal microscopy and Raman spectroscopy, enabling the point-to-point collection of three-dimensional nanoscale topographic information with the simultaneous reconstruction of corresponding chemical information. The microscope collects the scattered Raman light together with the Rayleigh light, both as Rayleigh scattered and reflected light (these are normally filtered out in conventional confocal Raman systems). Inherent in the design of the instrument is a significant improvement in the axial focusing resolution of topographical features in the image (to ∼1 nm), which, when coupled with super-resolution image restoration, gives a lateral resolution of 220 nm. By using differential confocal imaging for controlling the Raman imaging, the system presents a significant enhancement of the focusing and measurement accuracy, precision, and stability (with an antidrift capability), mitigating against both thermal and vibrational artefacts. We also demonstrate an improved scan speed, arising as a consequence of the nonaxial scanning mode

Suicide and suicide prevention in Asia

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