As there come to be more applications of intelligent robots, their task object is becoming more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We review the recent ...work on the feature sensing and robotic grasping of objects with uncertain information. In particular, we focus on how the robot perceives the features of an object, so as to reduce the uncertainty of objects, and how the robot completes object grasping through the learning-based approach when the traditional approach fails. The uncertain information is classified into geometric information and physical information. Based on the type of uncertain information, the object is further classified into three categories, which are geometric-uncertain objects, physical-uncertain objects, and unknown objects. Furthermore, the approaches to the feature sensing and robotic grasping of these objects are presented based on the varied characteristics of each type of object. Finally, we summarize the reviewed approaches for uncertain objects and provide some interesting issues to be more investigated in the future. It is found that the object’s features, such as material and compactness, are difficult to be sensed, and the object grasping approach based on learning networks plays a more important role when the unknown degree of the task object increases.
Power system cyber-physical uncertainties, including measurement ambiguities stemming from cyber attacks and data losses, along with system uncertainties introduced by massive renewables and complex ...dynamics, reduce the likelihood of enhancing the quality of measurements. Fortunately, denoising diffusion models exhibit powerful learning and generation abilities for the complex underlying physics of the real world. To this end, this paper proposes an improved detection and imputation based two-stage denoising diffusion model (TSDM) to identify and reconstruct the measurements with various cyber-physical uncertainties. The first stage of the model comprises a classifier-guided conditional anomaly detection component, while the second stage involves diffusion-based measurement imputation component. Moreover, the proposed TSDM adopts optimal variance to accelerate the diffusion generation process with subsequence sampling. Extensive numerical case studies demonstrate that the proposed TSDM can accurately recover power system measurements despite renewables-induced strong randomness and highly nonlinear dynamics. Additionally, the proposed TSDM has stronger robustness compared to existing reconstruction networks and exhibits lower computational complexity than general denoising diffusion models.
The focus of this article is on how parents of children with “nonnormative” genitalia cope with the conflict between the genital socialization process and their children’s genital autonomy in the ...Israeli medical–sociocultural context. Based on a qualitative narrative study that included 18 parents of children born with atypical genitalia and 23 parents who had chosen not to circumcise their sons, I compare parents’ experiences and perceptions of genital autonomy and examine the challenges posed by the Israeli genital socialization process from their perspective. In this study, I aim to shed light on the stressful and powerful Israeli genital socialization process, in which the medical, familial, and religious forces reproduce gendered normative genital appearances. The parents’ physical and emotional experiences include feelings of doubt and a critical stance toward genital socialization, changes in perceptions regarding genital appearance, and parental practices that challenge the meaning and outcomes of genital surgeries.
•Reliable simulations of chaotic motion of three body problem.•Propagation of micro-level physical uncertainty.•Physical limit of prediction of chaotic system.
A half century ago, Lorenz found the ...“butterfly effect” of chaotic dynamic systems and made his famous claim that long-term prediction of chaos is impossible. However, the meaning of the “long-term” in his claim is not very clear. In this article, a new concept, i.e. the physical limit of prediction time, denoted by Tpmax, is put forwarded to provide us a time-scale for at most how long mathematically reliable (numerical) simulations of trajectories of a chaotic dynamic system are physically correct. A special case of three-body problem is used as an example to illustrate that, due to the inherent, physical uncertainty of initial positions in the (dimensionless) micro-level of 10-60, the chaotic trajectories are essentially uncertain in physics after t>Tpmax, where Tpmax≈810 for this special case of the three body problem. Thus, physically, it has no sense to talk about the “accurate, deterministic prediction” of chaotic trajectories of the three body problem after t>Tpmax. In addition, our mathematically reliable simulations of the chaotic trajectories of the three bodies suggest that, due to the butterfly effect of chaotic dynamic systems, the micro-level physical uncertainty of initial conditions might transfer into macroscopic uncertainty. This suggests that micro-level uncertainty might be an origin of some macroscopic uncertainty. Besides, it might provide us a theoretical explanation about the origin of uncertainty (or randomness) of many macroscopic phenomena such as turbulent flows, the random distribution of stars in the universe, and so on.
For most structures, dependent failure is a common feature. It will lead to considerable errors or even misleading conclusion by neglecting failure dependence and assuming independent failure when ...estimating the structural reliability. In this paper, the reliability models of CCF structures caused by the randomness of load are developed and the estimation methods in consideration of both physical uncertainty and statistical uncertainty are proposed. Firstly, according to the LSI model and conditional probability method, reliability models of CCF component, CCF series system, CCF parallel system and CCF k/n system are derived. Secondly, the reliability point estimates of CCF structures are presented according to the maximum likelihood estimation of unknown parameters. Finally, in consideration of the statistical uncertainty of load and strength, a Monte-Carlo method is presented. The reliability samples of units from their reliability confidence distribution and reliability estimation function of CCF system are combined in the method to simulate CCF structural operation and obtain the interval estimate of structural reliability. This method has little limitation to the size and complexity of systems.
This chapter reviews recent work on children’s handling of uncertainty. Evidence from behavioural studies suggests that children are able to build alternative models of possible worlds under physical ...uncertainty (when there is currently no fact of the matter, e.g. a die that has yet to be thrown) but not epistemic uncertainty (where the outcome has been fixed, but is unknown, e.g. a die that has been thrown under a cup). Young children (5- to 7-year-olds) preferred to guess under epistemic uncertainty rather than physical, and were more likely to acknowledge that multiple outcomes were possible under the latter. However, when children were asked to explicitly evaluate their knowledge, there was no suggestion that they have recursive metacognitive understanding of physical or epistemic uncertainty. Manipulations of the task suggested that under epistemic uncertainty children tend to imagine one outcome when possible, and misinterpret this as the actual outcome. The chapter considers our results in the light of related biases in adults’ behaviour.
Uncertainties inherent in physical phenomena and structures obscure the physical representation of information. Time-dependent uncertainties in physical parameters are conventionally regarded as ...noise. Time-independent uncertainties, fixed in the physical structure of the devices used to represent information, although determinable in principle, are in fact unknown to the system designer and play a role in information theory similar to that of conventional noise. Several examples of the way in which power is used in logic and memory devices to overcome the effects of physical uncertainty are presented.
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