Simplified physically based model for estimating effective floc density

This paper applies dimensional analysis to propose an alternative model for estimating the effective density of flocs (Δρf). The model takes into account the effective density of the primary particles, in addition to the sizes of the floc and primary particles, and does not consider the concept of self-similarity. The model contains three dimensionless products and two empirical parameters (αf and βf), which were calibrated by using data available in the literature. Values of αf=0.7 and βf=0.8 were obtained. The average value of the primary particle size (Dp) for the data used in the analysis, inferred from the new model, was found to vary from 0.05 μm to 100 μm with a mean value of 2.5 μm. Good comparisons were obtained in comparing the estimated floc-settling velocity on the basis of the proposed model for effective floc density with the measured value.

Towards an integrative attachment-based model of relationship functioning

Based on attachment theory, this study developed a theory-based model of heterosexual relationship functioning that examined both proximal and distal factors and both actor and partner effects. A particular focus was on the underexplored issue of double-mediated effects between attachment orientation and relationship satisfaction. Data were collected from a community sample of 95 cohabiting and married couples with a mean age of 39.30 years. Participants completed measures of attachment, commitment, provision of partner support, trust, intimacy, destructive conflict management, and relationship satisfaction. The hypothesized model was largely supported. The association between attachment orientation and relationship satisfaction was mediated through a series of actor and partner variables. No gender differences were found across actor paths; however, differences were found in partner effects for men and women. The model has important implications for relationship researchers and practitioners. © 2013 The British Psychological Society.

Identifying affinity classes of inorganic materials binding sequences via a graph-based model

Rapid advances in bionanotechnology have recently generated growing interest in identifying peptides that bind to inorganic materials and classifying them based on their inorganic material affinities. However, there are some distinct characteristics of inorganic materials binding sequence data that limit the performance of many widely-used classification methods when applied to this problem. In this paper, we propose a novel framework to predict the affinity classes of peptide sequences with respect to an associated inorganic material. We first generate a large set of simulated peptide sequences based on an amino acid transition matrix tailored for the specific inorganic material. Then the probability of test sequences belonging to a specific affinity class is calculated by minimizing an objective function. In addition, the objective function is minimized through iterative propagation of probability estimates among sequences and sequence clusters. Results of computational experiments on two real inorganic material binding sequence data sets show that the proposed framework is highly effective for identifying the affinity classes of inorganic material binding sequences. Moreover, the experiments on the structural classification of proteins (SCOP) data set shows that the proposed framework is general and can be applied to traditional protein sequences.

Constitutive analysis of hot deformation behavior of a Ti6Al4V alloy using physical based model

The effect of deformation parameters on the flow behavior of a Ti6Al4V alloy has been studied to understand the deformation mechanisms during hot compression. Cylindrical samples with partially equiaxed grains were deformed in the α+β phase region at different thermo-mechanical conditions. To develop components with tailored properties, the physically based Estrin and Mecking (EM) model for the work hardening/dynamic recovery combined with the Avrami equation for dynamic recrystallization was used to predict the flow stress at varying process conditions. The EM model revealed good predictability up to the peak strain, however, at strain rates below 0.01s^-1, a higher B value was observed due to the reduced density of dislocation tangles. In contrast, the flow softening model revealed higher value of constants a and b at high strain rates due to the reduction in the volume fraction of dynamic recrystallization and larger peak strain. The predicted flow stress using the combined EM+Avrami model revealed good agreement with the measured flow stress resulted in very low average absolute relative error value. The microstructural analysis of the samples suggests the formation of coarse equiaxed grains together with the increased β phase fraction at low strain rate leads to a higher flow softening.

An audio signal based model for condition monitoring of sheet metal stamping process

Tool condition monitoring is an important factor in ensuring manufacturing efficiency and product quality. Audio signal based methods are a promising technique for condition monitoring. However, the influence of interfering signals and background noise has hindered the use of this technique in production sites. Blind signal separation (BSS) has the potential to solve this problem by recovering the signal of interest out of the observed mixtures, given that the knowledge about the BSS model is available. In this paper, we discuss the development of the BSS model for sheet metal stamping with a mechanical press system, so that the BSS techniques based on this model can be developed in future. This involves conducting a set of specially designed machine operations and developing a novel signal extraction technique. Also, the link between stamping process conditions and the extracted audio signal associated with stamping was successfully demonstrated by conducting a series of trials with different lubrication conditions and levels of tool wear.

A graph-based model for context-aware recommendation using implicit feedback data

Recommender systems have been successfully dealing with the problem of information overload. However, most recommendation methods suit to the scenarios where explicit feedback, e.g. ratings, are available, but might not be suitable for the most common scenarios with only implicit feedback. In addition, most existing methods only focus on user and item dimensions and neglect any additional contextual information, such as time and location. In this paper, we propose a graph-based generic recommendation framework, which constructs a Multi-Layer Context Graph (MLCG) from implicit feedback data, and then performs ranking algorithms in MLCG for context-aware recommendation. Specifically, MLCG incorporates a variety of contextual information into a recommendation process and models the interactions between users and items. Moreover, based on MLCG, two novel ranking methods are developed: Context-aware Personalized Random Walk (CPRW) captures user preferences and current situations, and Semantic Path-based Random Walk (SPRW) incorporates semantics of paths in MLCG into random walk model for recommendation. The experiments on two real-world datasets demonstrate the effectiveness of our approach.

Uplink power control via adaptive Hidden-Markov-Model-Based pathloss estimation

Dynamic variations in channel behavior is considered in transmission power control design for cellular radio systems. It is well known that power control increases system capacity, improves Quality of Service (QoS), and reduces multiuser interference. In this paper, an adaptive power control design based on the identification of the underlying pathloss dynamics of the fading channel is presented. Formulating power control decisions based on the measured received power levels allows modeling the fading channel pathloss dynamics in terms of a Hidden Markov Model (HMM). Applying the online HMM identification algorithm enables accurate estimation of the real pathloss ensuring efficient performance of the suggested power control scheme.

Damage identification scheme based on compressive sensing

Civil infrastructures are critical to every nation, due to their substantial investment, long service period, and enormous negative impacts after failure. However, they inevitably deteriorate during their service lives. Therefore, methods capable of assessing conditions and identifying damage in a structure timely and accurately have drawn increasing attention. Recently, compressive sensing (CS), a significant breakthrough in signal processing, has been proposed to capture and represent compressible signals at a rate significantly below the traditional Nyquist rate. Due to its sound theoretical background and notable influence, this methodology has been successfully applied in many research areas. In order to explore its application in structural damage identification, a new CS-based damage identification scheme is proposed in this paper, by regarding damage identification problems as pattern classification problems. The time domain structural responses are transferred to the frequency domain as sparse representation, and then the numerical simulated data under various damage scenarios will be used to train a feature matrix as input information. This matrix can be used for damage identification through an optimization process. This will be one of the first few applications of this advanced technique to structural engineering areas. In order to demonstrate its effectiveness, numerical simulation results on a complex pipe soil interaction model are used to train the parameters and then to identify the simulated pipe degradation damage and free-spanning damage. To further demonstrate the method, vibration tests of a steel pipe laid on the ground are carried out. The measured acceleration time histories are used for damage identification. Both numerical and experimental verification results confirm that the proposed damage identification scheme will be a promising tool for structural health monitoring.

An email classification model based on rough set theory

The communication via email is one of the most popular services of the Internet. Emails have brought us great convenience in our daily work and life. However, unsolicited messages or spam, flood our email boxes, which results in bandwidth, time and money wasting. To this end, this paper presents a rough set based model to classify emails into three categories - spam, no-spam and suspicious, rather than two classes (spam and non-spam) in most currently used approaches. By comparing with popular classification methods like Naive Bayes classification, the error ratio that a non-spam is discriminated to spam can be reduced using our proposed model.

Data-based dynamic haptic interaction model with deformable 3D objects

The data-based modeling of the haptic interaction simulation is a growing trend in research. These techniques offer a quick alternative to parametric modeling of the simulation. So far, most of the use of the data-based techniques was applied to static simulations. This paper introduces how to use data-based model in dynamic simulations. This ensures realistic behavior and produce results that are very close to parametric modeling. The results show that a quick and accurate response can be achieved using the proposed methods.

Static and dynamic tests for damage identification of a slab-girder structure

Slab-girder bridges are widely used in Australia. The shear connection between reinforced concrete slab and steel girder plays an important role in composite action. In order to test the suitability and efficiency of various vibration-based damage identification methods to assess the integrity of the structure, a scaled composite bridge model was constructed in the laboratory. Some removable shear connectors were specially designed and fabricated to link the beam and slab that were cast separately. In this test, two static loads were acted in the 1/3 points of the structure. In the first stage, dynamic test was conducted under different damage scenarios, where a number of shear connectors were removed step by step. In the second stage, the static load is increased gradually until concrete slab cracked. Static tests were conducted continuously to monitor the deflection and loading on the beam. Dynamic test was carried out before and after concrete cracking. Both static and dynamic results can be used to identify damage in the structure.

Damage identification of slab–girder structures: experimental studies

Slab–girder structures composed of steel girder and reinforced concrete slab are widely used in buildings and bridges in the world. Their advantages are largely based on the composite action through the shear connection between slab and girder. In order to assess the integrity of this kind of structures, numerous vibration-based damage identification methods have been proposed. In this study, a scaled composite slab–girder model was constructed in the laboratory. Some removable shear connectors were specially designed and fabricated to connect the girder and slab that were cast separately. Then, a two-stage experiment including both static and vibration tests was performed. In the first stage, vibration tests were conducted under different damage scenarios, where a certain number of shear connectors at certain locations were removed step by step. In the second stage, two sets of hydraulic loading equipment were used to apply four-point static loads in the test. The loads are increased gradually until concrete slab cracked. The loading histories as well as deflections at different points of the beam are recorded. Vibration test was carried out before and after concrete cracking. Experimental results show that the changes of mode shapes and relative displacement between slab and girder may be two promising parameters for damage identification of slab–girder structures.

A sequence based dynamic SOM model for text clustering

Text clustering can be considered as a four step process consisting of feature extraction, text representation, document clustering and cluster interpretation. Most text clustering models consider text as an unordered collection of words. However the semantics of text would be better captured if word sequences are taken into account.

In this paper we propose a sequence based text clustering model where four novel sequence based components are introduced in each of the four steps in the text clustering process.

Experiments conducted on the Reuters dataset and Sydney Morning Herald (SMH) news archives demonstrate the advantage of the proposed sequence based model, in terms of capturing context with semantics, accuracy and speed, compared to clustering of documents based on single words and n-gram based models.