Design, analysis, and control of a cable-driven parallel platform with a pneumatic muscle active support

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The neck is an important part of the body that connects the head to the torso, supporting the weight and generating the movement of the head. In this paper, a cable-driven parallel platform with a pneumatic muscle active support (CPPPMS) is presented for imitating human necks, where cable actuators imitate neck muscles and a pneumatic muscle actuator imitates spinal muscles, respectively. Analyzing the stiffness of the mechanism is carried out based on screw theory, and this mechanism is optimized according to the stiffness characteristics. While taking the dynamics of the pneumatic muscle active support into consideration as well as the cable dynamics and the dynamics of the Up-platform, a dynamic modeling approach to the CPPPMS is established. In order to overcome the flexibility and uncertainties amid the dynamic model, a sliding mode controller is investigated for trajectory tracking, and the stability of the control system is verified by a Lyapunov function. Moreover, a PD controller is proposed for a comparative study. The results of the simulation indicate that the sliding mode controller is more effective than the PD controller for the CPPPMS, and the CPPPMS provides feasible performances for operations under the sliding mode control

Out-of-plane instability and vibrations of a flexible circular arch under a moving load

Flexible lightweight arched structures are finding increasing use as components in smart engineering applications. Such structures are prone to various types of instability under moving transverse loads. Here, we study deformation and vibration of a hinged circular arch under a uniformly moving point load using geometrically-exact rod theory to allow for large pre- and post-buckling deformations. We first consider the quasi-statics problem, without inertia. We find that for arches with relatively large opening angle (∼ 160°) a sufficiently large traversing load will induce an out-of-plane flopping instability, instead of the in-plane collapse (snap-through) that dominates failure of arches with smaller opening angle. In a subsequent dynamics study, with full account of inertia, we then explore the effect of the speed of the load on this lateral buckling. We find speed to have a delaying (or even suppressing) effect on the onset of three-dimensional bending–torsional vibrations and instability. Based on numerical computations we propose a power law describing this effect. Our results highlight the role of inertia in the onset of elastic instability

A Proposed Theoretical Model of Discontinuous Usage of Voice-Activated Intelligent Personal Assistants (IPAs)

Based on the contradictory phenomenon of rapid development of Voice-Activated Intelligent Personal Assistants (Voice-Activated IPAs) and discontinuous usage of it, this paper investigates the antecedents of discontinuous usage of Voice-Activated IPAs. We first analyze the topic of Siri usage discussion from Zhihu\u27s Q&A website, and then propose a theoretical model which hypothesized that discontinuous usage of Voice-Activated IPAs are affected by perceived ambiguity, cognitive overload, privacy concern, social embarrassment and lack of integration. It is hypothesized that perceived ambiguity will exert nonlinear impacts on discontinuous usage. Meanwhile, perceived ambiguity is also affected by level of personification in a nonlinear way. Scale development and data collection would be conducted for the future work. It is expected that the results our research could provide theoretical and practical implications for the design of Voice-Activated IPAs

Theoretical and experimental investigations of the bifurcation behavior of creep groan of automotive disk brakes

There are several low frequency vibration phenomena which can be observed in automotive disk brakes. Creep groan is one of them provoking noise and structural vibrations of the car. In contrast to other vibration phenomena like brake squeal, creep groan is caused by the stick-slip-effect. A fundamental investigation of creep groan is proposed in this paper theoretically and experimentally with respect to parameter regions of the occurrence. Creep groan limit cycles are observed while performing experiments in a test rig with an idealized brake. A nonlinear model using the bristle friction law is set up in order to simulate the limit cycle of creep groan. As a result, the system shows three regions of qualitatively different behavior depending on the brake pressure and driving speed, i.e. a region with a stable equilibrium solution and a stable limit cycle, a region with only a stable equilibrium solution, and a region with only a stable limit cycle. The limit cycle can be interpreted as creep groan while the equilibrium solution is the desired vibration-free case. These three regions and the bifurcation behavior are demonstrated by the corresponding map. The experimental results are analyzed and compared with the simulation results showing good agreement. The bifurcation behavior and the corresponding map with three different regions are also confirmed by the experimental results. At the end, a similar map with the three regions is also measured at a test rig with a complete real brake

AdvLoRA: Adversarial Low-Rank Adaptation of Vision-Language Models

Vision-Language Models (VLMs) are a significant technique for Artificial General Intelligence (AGI). With the fast growth of AGI, the security problem become one of the most important challenges for VLMs. In this paper, through extensive experiments, we demonstrate the vulnerability of the conventional adaptation methods for VLMs, which may bring significant security risks. In addition, as the size of the VLMs increases, performing conventional adversarial adaptation techniques on VLMs results in high computational costs. To solve these problems, we propose a parameter-efficient \underline{Adv}ersarial adaptation method named \underline{AdvLoRA} by \underline{Lo}w-\underline{R}ank \underline{A}daptation. At first, we investigate and reveal the intrinsic low-rank property during the adversarial adaptation for VLMs. Different from LoRA, we improve the efficiency and robustness of adversarial adaptation by designing a novel reparameterizing method based on parameter clustering and parameter alignment. In addition, an adaptive parameter update strategy is proposed to further improve the robustness. By these settings, our proposed AdvLoRA alleviates the model security and high resource waste problems. Extensive experiments demonstrate the effectiveness and efficiency of the AdvLoRA

Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation

Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i.e., links in a graph) with items. To accommodate the different semantics of negative and positive links, existing works utilize two independent encoders to model users' positive and negative preferences, respectively. However, these approaches cannot learn the negative preferences from high-order heterogeneous interactions between users and items formed by multiple links with different signs, resulting in inaccurate and incomplete negative user preferences. To cope with these intractable issues, we propose a novel \textbf{L}ight \textbf{S}igned \textbf{G}raph Convolution Network specifically for \textbf{Rec}ommendation (\textbf{LSGRec}), which adopts a unified modeling approach to simultaneously model high-order users' positive and negative preferences on a signed user-item interaction graph. Specifically, for the negative preferences within high-order heterogeneous interactions, first-order negative preferences are captured by the negative links, while high-order negative preferences are propagated along positive edges. Then, recommendation results are generated based on positive preferences and optimized with negative ones. Finally, we train representations of users and items through different auxiliary tasks. Extensive experiments on three real-world datasets demonstrate that our method outperforms existing baselines regarding performance and computational efficiency. Our code is available at \url{https://anonymous.4open.science/r/LSGRec-BB95}