Optimisation of passive and semi-active heavy vehicle suspensions

An investigation into the potential for reducing road damage by optimising the design of heavy vehicle suspensions is described. In the first part of the paper two simple mathematical models are used to study the optimisation of conventional passive suspensions. Simple modifications are made to the steel spring suspension of a tandem axle trailer and it is found experimentally that RMS dynamic tyre forces can be reduced by 15% and theoretical road damage by 5.2%. A mathematical model of an air-sprung articulated vehicle is validated, and its suspension is optimised according to the simple models. This vehicle generates about 9% less damage than the leaf-sprung vehicle in the unmodified state and it is predicted that, for the operating conditions examined, the road damage caused by this vehicle can be reduced by a further 5.4%. Finally, it is shown experimentally that computer-controlled semi-active dampers have the potential to reduce road damage by a further 5-6%, compared to an air suspension with optimum passive damping. © Copyright 1994 Society of Automotive Engineers, Inc.

A Bayesian framework to predict deformations during supported excavations based on a semi-empirical method

The ground movements induced by the construction of supported excavation systems are generally predicted by empirical/semi-empirical methods in the design stage. However, these methods cannot account for the site-specific conditions and for information that becomes available as an excavation proceeds. A Bayesian updating methodology is proposed to update the predictions of ground movements in the later stages of excavation based on recorded deformation measurements. As an application, the proposed framework is used to predict the three-dimensional deformation shapes at four incremental excavation stages of an actual supported excavation project. © 2011 Taylor & Francis Group, London.

Reliability analysis of deep excavation based on a semi-empirical approach

The design and construction of deep excavations in urban environment is often governed by serviceability limit state related to the risk of damage to adjacent buildings. In current practice, the assessment of excavation-induced building damage has focused on a deterministic approach. This paper presents a component/system reliability analysis framework to assess the probability that specified threshold design criteria for multiple serviceability limit states are exceeded. A recently developed Bayesian probabilistic framework is used to update the predictions of ground movements in the later stages of excavation based on the recorded deformation measurements. An example is presented to show how the serviceability performance for excavation problems can be assessed based on the component/system reliability analysis. © 2011 ASCE.

A Bayesian framework to predict deformations during supported excavations based on a semi-empirical method

The ground movements induced by the construction of supported excavation systems are generally predicted in the design stage by empirical/semi-empirical methods. However, these methods cannot account for the site-specific conditions and for information that become available as an excavation proceeds. A Bayesian updating methodology is proposed to update the predictions of ground movements in the later stages of excavation based on recorded deformation measurements. As an application, the proposed framework is used to predict the three-dimensional deformation shapes at four incremental excavation stages of an actual supported excavation project. Copyright © ASCE 2011.

Rank-preserving geometric means of positive semi-definite matrices

The generalization of the geometric mean of positive scalars to positive definite matrices has attracted considerable attention since the seminal work of Ando. The paper generalizes this framework of matrix means by proposing the definition of a rank-preserving mean for two or an arbitrary number of positive semi-definite matrices of fixed rank. The proposed mean is shown to be geometric in that it satisfies all the expected properties of a rank-preserving geometric mean. The work is motivated by operations on low-rank approximations of positive definite matrices in high-dimensional spaces.© 2012 Elsevier Inc. All rights reserved.

Clustering behaviors in networks of integrate-and-fire oscillators

Clustering behavior is studied in a model of integrate-and-fire oscillators with excitatory pulse coupling. When considering a population of identical oscillators, the main result is a proof of global convergence to a phase-locked clustered behavior. The robustness of this clustering behavior is then investigated in a population of nonidentical oscillators by studying the transition from total clustering to the absence of clustering as the group coherence decreases. A robust intermediate situation of partial clustering, characterized by few oscillators traveling among nearly phase-locked clusters, is of particular interest. The analysis complements earlier studies of synchronization in a closely related model. © 2008 American Institute of Physics.

The Supervised IBP: Neighbourhood Preserving Infinite Latent Feature Models

We propose a probabilistic model to infer supervised latent variables in the Hamming space from observed data. Our model allows simultaneous inference of the number of binary latent variables, and their values. The latent variables preserve neighbourhood structure of the data in a sense that objects in the same semantic concept have similar latent values, and objects in different concepts have dissimilar latent values. We formulate the supervised infinite latent variable problem based on an intuitive principle of pulling objects together if they are of the same type, and pushing them apart if they are not. We then combine this principle with a flexible Indian Buffet Process prior on the latent variables. We show that the inferred supervised latent variables can be directly used to perform a nearest neighbour search for the purpose of retrieval. We introduce a new application of dynamically extending hash codes, and show how to effectively couple the structure of the hash codes with continuously growing structure of the neighbourhood preserving infinite latent feature space.

Distribution of 25 semi-volatile organic compounds of two urban lakes in Wuhan, China

In this study, the levels of 25 semi-volatile organic compounds (SVOCs) were measured in samples of water, suspended particulate matter (SPM) and sediment from two urban lakes in Wuhan, China. The total concentrations of 25 SVOCs varied from 529.4 to 2168.9 ng/L, 120.7 to 22543.7 ng/g dry weight and 1577.3 to 61579.6 ng/g dry wt. in water, SPM and sediment, respectively. The concentration of SVOCs in SPM was 9-10 times higher than that in water, and the concentration of SVOCs in sediment 1.5-2 times higher than that in SPM. The level of total SVOC25 in the samples from Moshuihu Lake was higher than that in Yuehu Lake. Among the 25 SVOCs, phthalate compounds were on the highest level in all observed samples ranging between 441.9-1831.2 ng/L, 116.3-17566.8 ng/g, dry wt. and 6432.8-48177.6 ng/g dry wt. in water, SPM and sediment, respectively. Bis(2-ethylhexyl)phthalate, the predominant component of the analyzed pollutants, was in the range from 246.7 to 537.5 ng/l, 51.2 to 15540.0 ng/g dry wt. and 468.2 to 45010.3 ng/g dry wt. in water, SPM and sediment, respectively. The content of PAHs, dinitrotoluene and isophoton in sediment was higher than that in water and SPM at most of the locations. The possible sources of the pollutants and their inter-relation with human activities were discussed.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.