329 research outputs found
Inference of the optimal probability distribution model for geotechnical parameters by using Weibull and NID distributions
To obtain the optimal probability distribution models of geotechnical parameters, the goodness of fit of the normal information diffusion (NID) distribution and Weibull distribution were investigated and compared for actual engineering samples and Monte Carlo (MC) simulated samples. Two datasets from actual engineering parameters (the strength of a rock mass and the average wind speed) were used to test the fitting abilities of these two distributions. The results show that the parameters of the NID distribution are easily estimated, the Kolmogorov-Smirnov (K-S) test results of the NID distribution are smaller than those of the Weibull distribution, and the NID distribution curves can perfectly reflect the stochastic volatility of geotechnical parameters with small sample sizes. The sample size effects on the fitting accuracy of the NID distribution and Weibull distribution were also investigated in this paper. Eight simulated samples with different sample sizes, namely, 15, 20, 30, 50, 100, 200, 500, and 1000, were produced by using the MC method based on two known Weibull distributions. The results show that with an increase in the sample size, the K-S test results of the NID distribution gradually decrease and tend to converge, while the chi-square test results of the NID distribution remain low and are always lower than those of the Weibull distribution. The cumulative probability values of the NID distribution are larger than those of the Weibull distribution and are always equal to 1.0000. Finally, the comparison of the fitting accuracy between the NID distribution and normalized Weibull distribution was also analyzed
Inferring Social Status and Rich Club Effects in Enterprise Communication Networks
Social status, defined as the relative rank or position that an individual
holds in a social hierarchy, is known to be among the most important motivating
forces in social behaviors. In this paper, we consider the notion of status
from the perspective of a position or title held by a person in an enterprise.
We study the intersection of social status and social networks in an
enterprise. We study whether enterprise communication logs can help reveal how
social interactions and individual status manifest themselves in social
networks. To that end, we use two enterprise datasets with three communication
channels --- voice call, short message, and email --- to demonstrate the
social-behavioral differences among individuals with different status. We have
several interesting findings and based on these findings we also develop a
model to predict social status. On the individual level, high-status
individuals are more likely to be spanned as structural holes by linking to
people in parts of the enterprise networks that are otherwise not well
connected to one another. On the community level, the principle of homophily,
social balance and clique theory generally indicate a "rich club" maintained by
high-status individuals, in the sense that this community is much more
connected, balanced and dense. Our model can predict social status of
individuals with 93% accuracy.Comment: 13 pages, 4 figure
Multi-sensor Suboptimal Fusion Student's Filter
A multi-sensor fusion Student's filter is proposed for time-series
recursive estimation in the presence of heavy-tailed process and measurement
noises. Driven from an information-theoretic optimization, the approach extends
the single sensor Student's Kalman filter based on the suboptimal
arithmetic average (AA) fusion approach. To ensure computationally efficient,
closed-form density recursion, reasonable approximation has been used in
both local-sensor filtering and inter-sensor fusion calculation. The overall
framework accommodates any Gaussian-oriented fusion approach such as the
covariance intersection (CI). Simulation demonstrates the effectiveness of the
proposed multi-sensor AA fusion-based filter in dealing with outliers as
compared with the classic Gaussian estimator, and the advantage of the AA
fusion in comparison with the CI approach and the augmented measurement fusion.Comment: 8 pages, 8 figure
A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking
[EN]We review some advances of the particle filtering (PF) algorithm that have been achieved
in the last decade in the context of target tracking, with regard to either a single target or multiple
targets in the presence of false or missing data. The first part of our review is on remarkable
achievements that have been made for the single-target PF from several aspects including importance
proposal, computing efficiency, particle degeneracy/impoverishment and constrained/multi-modal
systems. The second part of our review is on analyzing the intractable challenges raised within
the general multitarget (multi-sensor) tracking due to random target birth and termination, false
alarm, misdetection, measurement-to-track (M2T) uncertainty and track uncertainty. The mainstream
multitarget PF approaches consist of two main classes, one based on M2T association approaches and
the other not such as the finite set statistics-based PF. In either case, significant challenges remain due
to unknown tracking scenarios and integrated tracking management
Dynamic analysis of offset press gear-cylinder-bearing system applying finite element method
A dynamic model of offset press gear transmission system made up of gears, cylinders and bearings is proposed in this study. The model based on finite element method (FEM) includes some nonlinearity such as time-varying meshing stiffness, backlash, static transmission error and contact nonlinearity, which lead to complex nonlinear coupling. The Darren Bell principle and Lagrangian approach are applied to derive the motion equations of system, then the Newmark method is used to solve the equations for meshing force, acceleration, shoulder iron and rubber contact force. Eigenvalue solution is used to predict the critical speed, moreover, the influence of the radial and axial stiffness on the first-order critical speed is discussed. Considering the importance of acceleration and meshing force, the RMS value of acceleration and dynamic factor are also studied in this paper. The dynamic orbits of system are observed from the phase diagram, power spectrum, Lyapunov exponent and Poincare map. The figures clearly indicate that there are various forms of periodic and chaotic motions in different conditions. The simulation results show that with the increase of rotating speed, dynamic orbits transfer from periodic motion to chaotic motion in the cylinder discrete state
Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment
Large-scale vision-language models (VLMs), e.g., CLIP, learn broad visual
concepts from tedious training data, showing superb generalization ability.
Amount of prompt learning methods have been proposed to efficiently adapt the
VLMs to downstream tasks with only a few training samples. We introduce a novel
method to improve the prompt learning of vision-language models by
incorporating pre-trained large language models (LLMs), called Dual-Aligned
Prompt Tuning (DuAl-PT). Learnable prompts, like CoOp, implicitly model the
context through end-to-end training, which are difficult to control and
interpret. While explicit context descriptions generated by LLMs, like GPT-3,
can be directly used for zero-shot classification, such prompts are overly
relying on LLMs and still underexplored in few-shot domains. With DuAl-PT, we
propose to learn more context-aware prompts, benefiting from both explicit and
implicit context modeling. To achieve this, we introduce a pre-trained LLM to
generate context descriptions, and we encourage the prompts to learn from the
LLM's knowledge by alignment, as well as the alignment between prompts and
local image features. Empirically, DuAl-PT achieves superior performance on 11
downstream datasets on few-shot recognition and base-to-new generalization.
Hopefully, DuAl-PT can serve as a strong baseline. Code will be available
Exact algorithms to minimize interference in wireless sensor networks
AbstractFinding a low-interference connected topology is a fundamental problem in wireless sensor networks (WSNs). The problem of reducing interference through adjusting the nodes’ transmission radii in a connected network is one of the most well-known open algorithmic problems in wireless sensor network optimization. In this paper, we study minimization of the average interference and the maximum interference for the highway model, where all the nodes are arbitrarily distributed on a line. First, we prove that there is always an optimal topology with minimum interference that is planar. Then, two exact algorithms are proposed. The first one is an exact algorithm to minimize the average interference in polynomial time, O(n3Δ), where n is the number of nodes and Δ is the maximum node degree. The second one is an exact algorithm to minimize the maximum interference in sub-exponential time, O(n3ΔO(k)), where k=O(Δ) is the minimum maximum interference. All the optimal topologies constructed are planar
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