249 research outputs found
Simultaneous state and input estimation with application to a two-link robotic system
This paper addresses the problem of estimating simultaneously the state and input of a nonlinear system with application to a two link robotic manipulator - the Pendubot. The system nonlinearity comprises a Lipschitz function with respect to the state, and a nonlinear term which is a function of both the state and input. It is shown that under some conditions, an observer can be designed to estimate simultaneously the system’s state and input. Simulation and experimental results, obtained around the inverted equilibrium position, are presented to demonstrate the validity of the approach.<br /
CURRENT SITUATION OF STUDENTS’ PSYCHOLOGICAL STATE BEFORE PRACTICAL COURSES’ FINAL EXAMS
The article aimed to determine the factors influencing students’ psychological state before the final exam of practical courses. The article used conventional scientific research methods in sports and physical training combined with psychological tests studied on fifty students at Ho Chi Minh City University of Physical Education and Sports (UPES). After reviewing related studies and consulting with experts, four tests were employed to assess the psychological state before the test of the research subjects. The results showed that students with a good psychological state to take the exam had good test results. Conversely, students with a feverish or lethargic state will have poor test results. The research results serve as the basis for proposing measures to adjust the psychological state before the exam, contributing to improving the learning results of students. Article visualizations
When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach
Rate Splitting Multiple Access (RSMA) has emerged as an effective
interference management scheme for applications that require high data rates.
Although RSMA has shown advantages in rate enhancement and spectral efficiency,
it has yet not to be ready for latency-sensitive applications such as virtual
reality streaming, which is an essential building block of future 6G networks.
Unlike conventional High-Definition streaming applications, streaming virtual
reality applications requires not only stringent latency requirements but also
the computation capability of the transmitter to quickly respond to dynamic
users' demands. Thus, conventional RSMA approaches usually fail to address the
challenges caused by computational demands at the transmitter, let alone the
dynamic nature of the virtual reality streaming applications. To overcome the
aforementioned challenges, we first formulate the virtual reality streaming
problem assisted by RSMA as a joint communication and computation optimization
problem. A novel multicast approach is then proposed to cluster users into
different groups based on a Field-of-View metric and transmit multicast streams
in a hierarchical manner. After that, we propose a deep reinforcement learning
approach to obtain the solution for the optimization problem. Extensive
simulations show that our framework can achieve the millisecond-latency
requirement, which is much lower than other baseline schemes
Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach
Toward user-driven Metaverse applications with fast wireless connectivity and
tremendous computing demand through future 6G infrastructures, we propose a
Brain-Computer Interface (BCI) enabled framework that paves the way for the
creation of intelligent human-like avatars. Our approach takes a first step
toward the Metaverse systems in which the digital avatars are envisioned to be
more intelligent by collecting and analyzing brain signals through cellular
networks. In our proposed system, Metaverse users experience Metaverse
applications while sending their brain signals via uplink wireless channels in
order to create intelligent human-like avatars at the base station. As such,
the digital avatars can not only give useful recommendations for the users but
also enable the system to create user-driven applications. Our proposed
framework involves a mixed decision-making and classification problem in which
the base station has to allocate its computing and radio resources to the users
and classify the brain signals of users in an efficient manner. To this end, we
propose a hybrid training algorithm that utilizes recent advances in deep
reinforcement learning to address the problem. Specifically, our hybrid
training algorithm contains three deep neural networks cooperating with each
other to enable better realization of the mixed decision-making and
classification problem. Simulation results show that our proposed framework can
jointly address resource allocation for the system and classify brain signals
of the users with highly accurate predictions
Optimizing Boiler Efficiency by Data Mining Teciques: A Case Study
In a fertilizer plant, the steam boiler is the most important component. In order to keep the plant operating in the effective mode, the boiler efficiency must be observed continuously by several operators. When the trend of the boiler efficiency is going down, they may adjust the controlling parameters of the boiler to increase its efficiency. Since manual operation usually leads to unex-pectedly mistakes and hurts the efficiency of the system, we build an information system that plays the role of the operators in observing the boiler and adjusting the controlling parameters to stabilize the boiler efficiency. In this paper, we first introduce the architecture of the information system. We then present how to apply K-means and Fuzzy C-means algorithms to derive a knowledge base from the historical operational data of the boiler. Next, recurrent fuzzy neural network is employed to build a boiler simulator for evaluating which tuple of input values is the best optimal and then automatically adjusting controlling inputs of the boiler by the optimal val-ues. In order to prove the effectiveness of our system, we deployed it at Phu My Fertilizer Plant equipped with MARCHI boiler having capacity of 76-84 ton/h. We found that our system have improved the boiler efficiency about 0.28-1.12% in average and brought benefit about 57.000 USD/year to the Phu My Fertilizer Plant
Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach
The ability to sense, localize, and estimate the 3D position and orientation
of the human body is critical in virtual reality (VR) and extended reality (XR)
applications. This becomes more important and challenging with the deployment
of VR/XR applications over the next generation of wireless systems such as 5G
and beyond. In this paper, we propose a novel framework that can reconstruct
the 3D human body pose of the user given sparse measurements from Inertial
Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically,
our framework enables reliable transmission of compressed IMU signals through
noisy wireless channels and effective recovery of such signals at the receiver,
e.g., an edge server. This task is very challenging due to the constraints of
transmit power, recovery accuracy, and recovery latency. To address these
challenges, we first develop a deep generative model at the receiver to recover
the data from linear measurements of IMU signals. The linear measurements of
the IMU signals are obtained by a linear projection with a measurement matrix
based on the compressive sensing theory. The key to the success of our
framework lies in the novel design of the measurement matrix at the
transmitter, which can not only satisfy power constraints for the IMU devices
but also obtain a highly accurate recovery for the IMU signals at the receiver.
This can be achieved by extending the set-restricted eigenvalue condition of
the measurement matrix and combining it with an upper bound for the power
transmission constraint. Our framework can achieve robust performance for
recovering 3D human poses from noisy compressed IMU signals. Additionally, our
pre-trained deep generative model achieves signal reconstruction accuracy
comparable to an optimization-based approach, i.e., Lasso, but is an order of
magnitude faster
Enhancing Few-shot Image Classification with Cosine Transformer
This paper addresses the few-shot image classification problem, where the
classification task is performed on unlabeled query samples given a small
amount of labeled support samples only. One major challenge of the few-shot
learning problem is the large variety of object visual appearances that
prevents the support samples to represent that object comprehensively. This
might result in a significant difference between support and query samples,
therefore undermining the performance of few-shot algorithms. In this paper, we
tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the
relational map between supports and queries is effectively obtained for the
few-shot tasks. The FS-CT consists of two parts, a learnable prototypical
embedding network to obtain categorical representations from support samples
with hard cases, and a transformer encoder to effectively achieve the
relational map from two different support and query samples. We introduce
Cosine Attention, a more robust and stable attention module that enhances the
transformer module significantly and therefore improves FS-CT performance from
5% to over 20% in accuracy compared to the default scaled dot-product
mechanism. Our method performs competitive results in mini-ImageNet, CUB-200,
and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and
few-shot configurations. We also developed a custom few-shot dataset for Yoga
pose recognition to demonstrate the potential of our algorithm for practical
application. Our FS-CT with cosine attention is a lightweight, simple few-shot
algorithm that can be applied for a wide range of applications, such as
healthcare, medical, and security surveillance. The official implementation
code of our Few-shot Cosine Transformer is available at
https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme
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