Cross-layer optimization in TCP/IP networks

TCP-AQM can be interpreted as distributed primal-dual algorithms to maximize aggregate utility over source rates. We show that an equilibrium of TCP/IP, if exists, maximizes aggregate utility over both source rates and routes, provided congestion prices are used as link costs. An equilibrium exists if and only if this utility maximization problem and its Lagrangian dual have no duality gap. In this case, TCP/IP incurs no penalty in not splitting traffic across multiple paths. Such an equilibrium, however, can be unstable. It can be stabilized by adding a static component to link cost, but at the expense of a reduced utility in equilibrium. If link capacities are optimally provisioned, however, pure static routing, which is necessarily stable, is sufficient to maximize utility. Moreover single-path routing again achieves the same utility as multipath routing at optimality

Intrinsic energy conversion mechanism via telescopic extension and retraction of concentric carbon nanotubes

The conversion of other forms of energy into mechanical work through the geometrical extension and retraction of nanomaterials has a wide variety of potential applications, including for mimicking biomotors. Here, using molecular dynamic simulations, we demonstrate that there exists an intrinsic energy conversion mechanism between thermal energy and mechanical work in the telescopic motions of double-walled carbon nanotubes (DWCNTs). A DWCNT can inherently convert heat into mechanical work in its telescopic extension process, while convert mechanical energy into heat in its telescopic retraction process. These two processes are thermodynamically reversible. The underlying mechanism for this reversibility is that the entropy changes with the telescopic overlapping length of concentric individual tubes. We find also that the entropy effect enlarges with the decreasing intertube space of DWCNTs. As a result, the spontaneously telescopic motion of a condensed DWCNT can be switched to extrusion by rising the system temperature above a critical value. These findings are important for fundamentally understanding the mechanical behavior of concentric nanotubes, and may have general implications in the application of DWCNTs as linear motors in nanodevices

Microbial immobilization technology for remediation of petroleum hydrocarbon contaminated soil

Petroleum hydrocarbon is a kind of global pollutant that is difficult to degrade. The remediation of petroleum hydrocarbon contaminated soil has always been a challenging subject for environmentalists. Microbial immobilization technology (MIT) has the advantages of high efficiency, stability, low cost and environmental friendliness. It shows great application potential in soil remediation. In recent years, the study of microbial immobilization technology for remediation of petroleum hydrocarbon contaminated soil is in the ascendant. Microbial immobilization technology has become an effective way to improve microbial degradation of petroleum hydrocarbons in soil. This paper discusses the research progress of microbial immobilization technology, summarizes the different characteristics of carrier materials, microorganisms, immobilization methods and influencing factors in the immobilization process and their effects on the immobilization effect, and expounds the research status and development trend of immobilization technology for the remediation of petroleum hydrocarbon contaminated soil

STGlow: A Flow-based Generative Framework with Dual Graphormer for Pedestrian Trajectory Prediction

Pedestrian trajectory prediction task is an essential component of intelligent systems, and its applications include but are not limited to autonomous driving, robot navigation, and anomaly detection of monitoring systems. Due to the diversity of motion behaviors and the complex social interactions among pedestrians, accurately forecasting the future trajectory of pedestrians is challenging. Existing approaches commonly adopt GANs or CVAEs to generate diverse trajectories. However, GAN-based methods do not directly model data in a latent space, which makes them fail to have full support over the underlying data distribution; CVAE-based methods optimize a lower bound on the log-likelihood of observations, causing the learned distribution to deviate from the underlying distribution. The above limitations make existing approaches often generate highly biased or unnatural trajectories. In this paper, we propose a novel generative flow based framework with dual graphormer for pedestrian trajectory prediction (STGlow). Different from previous approaches, our method can more accurately model the underlying data distribution by optimizing the exact log-likelihood of motion behaviors. Besides, our method has clear physical meanings to simulate the evolution of human motion behaviors, where the forward process of the flow gradually degrades the complex motion behavior into a simple behavior, while its reverse process represents the evolution of a simple behavior to the complex motion behavior. Further, we introduce a dual graphormer combining with the graph structure to more adequately model the temporal dependencies and the mutual spatial interactions. Experimental results on several benchmarks demonstrate that our method achieves much better performance compared to previous state-of-the-art approaches.Comment: 12 pages, 8 figure

Development of an indirect bridge health monitoring approach using moving sensors

University of Technology Sydney. Faculty of Engineering and Information Technology.The inevitable deterioration and damage of bridge infrastructures due to repeated and excess traffic loading, environmental erosion and ageing are of great concern worldwide. Bridge structural health monitoring (SHM) is critical to obtain structural health information and early warning for potential damage. Most of the current SHM strategies measure vibration responses from sensors installed at different locations on the bridge. This direct approach poses several challenges, such as the high cost of the installation and maintenance of sensors, the need for extensive data processing and the insufficient spatial information. To seek a more economical and flexible way to monitor bridges, an indirect approach that measures responses of a passing vehicle has recently drawn great attention. This strategy involves the use of instrumented vehicles as a moving sensory system to capture bridge dynamic information via vehicle-bridge interaction (VBI). Sensors are installed on the vehicle axles or body. However, the responses from sensors on a moving vehicle are nonstationary, noisy and significantly affected by the surface roughness of the bridge. Therefore, most of the classical output-only system identification approaches based on the assumption of white noise excitation may fail to extract accurate structural dynamic properties. This research aims to establish a framework for bridge SHM using vehicle-based mobile sensory systems. Indirect structural identification methods that consider the intrinsic nonstationary characteristics of VBI responses are proposed to extract the bridge dynamic parameters from vehicle acceleration responses. Firstly, a Short-time Stochastic Subspace Identification (STSSI) strategy was proposed to identify bridge modal frequencies and mode shapes. This method combines conventional SSI with a rescale procedure to estimate the bridge modal parameters using the responses of two instrumented vehicles. Secondly, based on the sequential implementation of singular spectrum analysis (SSA) and blind source separation (BSS), a method named drive-by blind modal identification with singular spectrum analysis (SSA-BSS) was proposed to extract the response components from a single set of vehicle vibration responses. The bridge frequencies can be identified from the obtained bridge related components. Numerical and experimental results clearly demonstrated the feasibility and effectiveness of the proposed methods for indirect identification of bridge modal frequencies and mode shapes. To gain insight on the time-dependent features of VBI system, a time-frequency (TF) analysis method called Synchroextracting transform (SET) was used to analyse the vehicle and bridge responses in TF domain. The instantaneous frequencies (IFs) of the system revealed the time-varying characteristics of the VBI system. Besides the indirect bridge modal identification, a two-step drive-by bridge damage detection strategy using vehicle axle responses was proposed. Dual Kalman filter (DKF) was applied to identify the interaction forces between vehicle and bridge. With the interaction forces, a sensitivity analysis was performed with regularization technique to identify the bridge damage. The proposed two-step damage detection method effectively identified the location and extent of the damages using vehicle axle responses, which demonstrated its great potential for drive-by bridge damage detection. Moreover, the SSA-BSS and the TF analysis strategy were successfully applied to analyse the responses from an in-situ VBI system. In summary, an indirect bridge SHM technique using vehicle-based moving sensing system was developed in this study. Bridge modal identification and damage detection were conducted successfully using vehicle responses. Results further demonstrated that it can be a convenient and cost-effective alternative or a promising complement to conventional bridge SHM