409 research outputs found
Oscillation of Third-Order Neutral Delay Differential Equations
The purpose of this paper is to examine oscillatory properties of the third-order neutral delay differential equation [a(t)(b(t)(x(t)+p(t)x(σ(t)))′)′]′+q(t)x(τ(t))=0. Some oscillatory and asymptotic criteria are presented. These criteria improve and complement those results in the literature. Moreover, some examples are given to illustrate the main results
Properties of higher-order half-linear functional differential equations with noncanonical operators
A Critical Escape Probability Formulation for Enhancing the Transient Stability of Power Systems with System Parameter Design
For the enhancement of the transient stability of power systems, the key is
to define a quantitative optimization formulation with system parameters as
decision variables. In this paper, we model the disturbances by Gaussian noise
and define a metric named Critical Escape Probability (CREP) based on the
invariant probability measure of a linearised stochastic processes. CREP
characterizes the probability of the state escaping from a critical set. CREP
involves all the system parameters and reflects the size of the basin of
attraction of the nonlinear systems. An optimization framework that minimizes
CREP with the system parameters as decision variablesis is presented.
Simulations show that the mean first hitting time when the state hits the
boundary of the critical set, that is often used to describe the stability of
nonlinear systems, is dramatically increased by minimizing CREP. This indicates
that the transient stability of the system is effectively enhanced. It also
shown that suppressing the state fluctuations only is insufficient for
enhancing the transient stability. In addition, the famous Braess' paradox
which also exists in power systems is revisited. Surprisingly, it turned out
that the paradoxes identified by the traditional metric may not exist according
to CREP. This new metric opens a new avenue for the transient stability
analysis of future power systems integrated with large amounts of renewable
energy.Comment: 15 pages, 4 figures, 2 table
Input-To-State Stability for a Class of Switched Stochastic Nonlinear Systems by an Improved Average Dwell Time Method
This paper investigates the input-to-state stable in the mean (ISSiM) property of the switched stochastic nonlinear (SSN) systems with an improved average dwell time (ADT) method in two cases: (i) all of the constituent subsystems are ISSiM and (ii) parts of the constituent subsystems are ISSiM. First, an improved ADT method for stability of SSN systems is introduced. Then, based on that not only a new ISSiM result for SSN systems whose subsystems are ISSiM is presented, but also a new
ISSiM result for such systems in which parts of subsystems are ISSiM is established. In comparison with the existing ones, the main results obtained in this paper have some advantages. Finally, an illustrative example with numerical simulation is verified the correctness and validity of the proposed results
Input-To-State Stability for a Class of Switched Stochastic Nonlinear Systems by an Improved Average Dwell Time Method
This paper investigates the input-to-state stable in the mean (ISSiM) property of the switched stochastic nonlinear (SSN) systems with an improved average dwell time (ADT) method in two cases: (i) all of the constituent subsystems are ISSiM and (ii) parts of the constituent subsystems are ISSiM. First, an improved ADT method for stability of SSN systems is introduced. Then, based on that not only a new ISSiM result for SSN systems whose subsystems are ISSiM is presented, but also a new ISSiM result for such systems in which parts of subsystems are ISSiM is established. In comparison with the existing ones, the main results obtained in this paper have some advantages. Finally, an illustrative example with numerical simulation is verified the correctness and validity of the proposed results
NetGPT: A Native-AI Network Architecture Beyond Provisioning Personalized Generative Services
Large language models (LLMs) have triggered tremendous success to empower
daily life by generative information, and the personalization of LLMs could
further contribute to their applications due to better alignment with human
intents. Towards personalized generative services, a collaborative cloud-edge
methodology sounds promising, as it facilitates the effective orchestration of
heterogeneous distributed communication and computing resources. In this
article, after discussing the pros and cons of several candidate cloud-edge
collaboration techniques, we put forward NetGPT to capably deploy appropriate
LLMs at the edge and the cloud in accordance with their computing capacity. In
addition, edge LLMs could efficiently leverage location-based information for
personalized prompt completion, thus benefiting the interaction with cloud
LLMs. After deploying representative open-source LLMs (e.g., GPT-2-base and
LLaMA model) at the edge and the cloud, we present the feasibility of NetGPT on
the basis of low-rank adaptation-based light-weight fine-tuning. Subsequently,
we highlight substantial essential changes required for a native artificial
intelligence (AI) network architecture towards NetGPT, with special emphasis on
deeper integration of communications and computing resources and careful
calibration of logical AI workflow. Furthermore, we demonstrate several
by-product benefits of NetGPT, given edge LLM's astonishing capability to
predict trends and infer intents, which possibly leads to a unified solution
for intelligent network management \& orchestration. In a nutshell, we argue
that NetGPT is a promising native-AI network architecture beyond provisioning
personalized generative services
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