35 research outputs found
A Drift Eliminated Attitude & Position Estimation Algorithm In 3D
Inertial wearable sensors constitute a booming industry. They are self contained, low powered and highly miniaturized. They allow for remote or self monitoring of health-related parameters. When used to obtain 3-D position, velocity and orientation information, research has shown that it is possible to draw conclusion about issues such as fall risk, Parkinson disease and gait assessment.
A key issues in extracting information from accelerometers and gyroscopes is the fusion of their noisy data to allow accurate assessment of the disease. This, so far, is an unsolved problem. Typically, a Kalman filter or its nonlinear, non-Gaussian version are implemented for estimating attitude â?? which in turn is critical for position estimation. However, sampling rates and large state vectors required make them unacceptable for the limited-capacity batteries of low-cost wearable sensors.
The low-computation cost complementary filter has recently been re-emerging as the algorithm for attitude estimation. We employ it with a heuristic drift elimination method that is shown to remove, almost entirely, the drift caused by the gyroscope and hence generate a fairly accurate attitude and drift-eliminated position estimate.
Inertial sensor data is obtained from the 10-axis SP-10C sensor, attached to a wearable insole that is inserted in the shoe. Data is obtained from walking in a structured indoor environment in Votey Hall
Joint Downlink Base Station Association and Power Control for Max-Min Fairness: Computation and Complexity
In a heterogeneous network (HetNet) with a large number of low power base
stations (BSs), proper user-BS association and power control is crucial to
achieving desirable system performance. In this paper, we systematically study
the joint BS association and power allocation problem for a downlink cellular
network under the max-min fairness criterion. First, we show that this problem
is NP-hard. Second, we show that the upper bound of the optimal value can be
easily computed, and propose a two-stage algorithm to find a high-quality
suboptimal solution. Simulation results show that the proposed algorithm is
near-optimal in the high-SNR regime. Third, we show that the problem under some
additional mild assumptions can be solved to global optima in polynomial time
by a semi-distributed algorithm. This result is based on a transformation of
the original problem to an assignment problem with gains , where
are the channel gains.Comment: 24 pages, 7 figures, a shorter version submitted to IEEE JSA
A Survey on Users' Perspectives to Functionalities of Web-Based Construction Collaboration Extranets
Construction collaboration extranets (CCEs) provide various functionalities depending on the vendors' origins, history, experiences, and financial status. Previous research has listed and described the functionalities that extranet systems can be capable of providing. However, no publication was found so far to systematically analyze users' perspectives to the provided functionalities. This article is to bridge this gap through a questionnaire survey to the users. It aims at examining user's attitude to functionalities of CCEs. The result may be useful to information system vendors, end-users and researchers involved in CCEs development and implementation
PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization
Deep neural networks (DNNs) are vulnerable to adversarial attacks. It is
found empirically that adversarially robust generalization is crucial in
establishing defense algorithms against adversarial attacks. Therefore, it is
interesting to study the theoretical guarantee of robust generalization. This
paper focuses on norm-based complexity, based on a PAC-Bayes approach
(Neyshabur et al., 2017). The main challenge lies in extending the key
ingredient, which is a weight perturbation bound in standard settings, to the
robust settings. Existing attempts heavily rely on additional strong
assumptions, leading to loose bounds. In this paper, we address this issue and
provide a spectrally-normalized robust generalization bound for DNNs. Compared
to existing bounds, our bound offers two significant advantages: Firstly, it
does not depend on additional assumptions. Secondly, it is considerably
tighter, aligning with the bounds of standard generalization. Therefore, our
result provides a different perspective on understanding robust generalization:
The mismatch terms between standard and robust generalization bounds shown in
previous studies do not contribute to the poor robust generalization. Instead,
these disparities solely due to mathematical issues. Finally, we extend the
main result to adversarial robustness against general non- attacks and
other neural network architectures.Comment: NeurIPS 202
A Full Characterization of Excess Risk via Empirical Risk Landscape
In this paper, we provide a unified analysis of the excess risk of the model
trained by a proper algorithm with both smooth convex and non-convex loss
functions. In contrast to the existing bounds in the literature that depends on
iteration steps, our bounds to the excess risk do not diverge with the number
of iterations. This underscores that, at least for smooth loss functions, the
excess risk can be guaranteed after training. To get the bounds to excess risk,
we develop a technique based on algorithmic stability and non-asymptotic
characterization of the empirical risk landscape. The model obtained by a
proper algorithm is proved to generalize with this technique. Specifically, for
non-convex loss, the conclusion is obtained via the technique and analyzing the
stability of a constructed auxiliary algorithm. Combining this with some
properties of the empirical risk landscape, we derive converged upper bounds to
the excess risk in both convex and non-convex regime with the help of some
classical optimization results.Comment: 38page
Adversarial Rademacher Complexity of Deep Neural Networks
Deep neural networks are vulnerable to adversarial attacks. Ideally, a robust
model shall perform well on both the perturbed training data and the unseen
perturbed test data. It is found empirically that fitting perturbed training
data is not hard, but generalizing to perturbed test data is quite difficult.
To better understand adversarial generalization, it is of great interest to
study the adversarial Rademacher complexity (ARC) of deep neural networks.
However, how to bound ARC in multi-layers cases is largely unclear due to the
difficulty of analyzing adversarial loss in the definition of ARC. There have
been two types of attempts of ARC. One is to provide the upper bound of ARC in
linear and one-hidden layer cases. However, these approaches seem hard to
extend to multi-layer cases. Another is to modify the adversarial loss and
provide upper bounds of Rademacher complexity on such surrogate loss in
multi-layer cases. However, such variants of Rademacher complexity are not
guaranteed to be bounds for meaningful robust generalization gaps (RGG). In
this paper, we provide a solution to this unsolved problem. Specifically, we
provide the first bound of adversarial Rademacher complexity of deep neural
networks. Our approach is based on covering numbers. We provide a method to
handle the robustify function classes of DNNs such that we can calculate the
covering numbers. Finally, we provide experiments to study the empirical
implication of our bounds and provide an analysis of poor adversarial
generalization
Carbon Nanostructures Production by AC Arc Discharge Plasma Process at Atmospheric Pressure
Carbon nanostructures have received much attention for a wide range of applications. In this paper, we produced carbon nanostructures by decomposition of benzene using AC arc discharge plasma process at atmospheric pressure. Discharge was carried out at a voltage of 380 V, with a current of 6 A–20 A. The products were characterized by scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), powder X-ray diffraction (XRD), and Raman spectra. The results show that the products on the inner wall of the reactor and the sand core are nanoparticles with 20–60 nm diameter, and the products on the electrode ends are nanoparticles, agglomerate carbon particles, and multiwalled carbon nanotubes (MWCNTs). The maximum yield content of carbon nanotubes occurs when the arc discharge current is 8 A. Finally, the reaction mechanism was discussed