Zeta Potential of Aggregate and Dynamic Modulus of HMA Estimation Using Aggregate Silica Content

The mineralogical composition of an aggregate influences its adhesion with bitumen and therefore its dynamic modulus. However, few studies have been conducted on this aspect. One of the most used properties to describe the impact of aggregate on the adhesiveness phenomena is the zeta potential. In this study, the first mineralogical and chemical properties were considered through the percentage of silica in the rock source of aggregates and the electric aggregate particles charge zeta. Dynamic modulus values used for regression process are determined from complex modulus test on nine asphalt concretes mix designed with aggregate types (basalt of Diack, quartzite of Bakel and Limestone of Bandia). The results showed that aggregate with high percentage of silica have higher zeta potential than aggregate with low percentage of silica. The development of a zeta potential predictive model showed a strong sensitivity to silica. The results of the complex modulus tests showed that Hot Mixture Asphalt (HMA) mixed with aggregate containing high silica contents gave better results than those mixed with aggregates containing low percentage of silica. The dynamic modulus predictive models of HMA developed shows that it is the properties of bitumen that influence more. However, the effect of silica although low, is very marked at low temperatures and high frequencies.

Efficient and Innovative Techniques for Collective Acquisition of Weak GNSS Signals

Navigation and positioning in harsh environments is still a great challenge for many applications. Collective Detection (CD) is a powerful approach for acquiring highly attenuated satellite signals in challenging environments, because of its capacity to process all visible satellites collectively taking advantage of the spatial correlation between GNSS signals as a vector acquisition scheme. CD combines the correlator outputs of satellite channels and projects them onto the position/clock bias domain in order to enhance the overall GNSS signal detection probability. In CD, the code phase search for all satellites in view is mapped into a receiver position/clock bias grid and the satellite signals are not acquired individually but collectively. In this concept, a priori knowledge of satellite ephemeris and reference location are provided to the user. Furthermore, CD addresses some of the inherent drawbacks of the conventional acquisition at the expenses of an increased computational cost. CD techniques are computationally intensive because of the significant number of candidate points in the position-time domain. The aim of this paper is to describe the operation of the CD approach incorporating new methods and architectures to address both the complexity and sensitivity problems. The first method consists of hybridizing the collective detection approach with some correlation techniques and coupling it with a better technique for Doppler frequency estimate. For that, a new scheme with less calculation load is proposed in order to accelerate the detection and location process. Then, high sensitivity acquisition techniques using long coherent integration and non-coherent integration are used in order to improve the performance of the CD algorithm.

X-ray Computed Tomography Characterization of 3D Tufted Twill Textile Composite for Aerostructures

Damage assessments in three dimensional (3D) textile composites subjected to mechanical loading can be performed by non-destructive and destructive techniques.This paper applies the two techniques to investigate the fracture behavior of 3D tufted textile composites.X-ray computed tomography as a non-destructive evaluation method is appropriate to detect damage locations and identify their progression in 3D textile composites.Destructive methods such as sectioning toward observing damage provide valuable information about damage patterns.The results of this research could be utilized to evaluate the initial cause of rupture in 3D tufted composites used in aerospace structures and analyze fracture modes and damage progression.

QL-CBR Hybrid Approach for Adapting Context-Aware Services

A context-aware service in a smart environment aims to supply services according to user situational information,which changes dynamically.Most existing context-aware systems provide context-aware services based on supervised algorithms.Reinforcement algorithms are another type of machine-learning algorithm that have been shown to be useful in dynamic environments through trialand-error interactions.They also have the ability to build excellent self-adaptive systems.In this study,we aim to incorporate reinforcement algorithms(Q-learning)into a context-aware system to provide relevant services based on a user’s dynamic context.To accelerate the convergence of reinforcement learning(RL)algorithms and provide the correct services in real situations,we propose a combination of the Q-learning and case-based reasoning(CBR)algorithms.We then analyze how the incorporation of CBR enables Q-learning to become more effi-cient and adapt to changing environments by continuously producing suitable services.Simulation results demonstrate the effectiveness of the proposed approach compared to the traditional CBR approach.

Performance Optimization of Diesel Generators Using Permanent Magnet Synchronous Generator with Rotating Stator

One of the solutions to reduce fuel consumption of diesel generators (DG) is to adapt the rotational speed to mechanical torque of the crankshaft. When load power decreases, a reduction in both mechanical torque and rotational speed of the diesel engine will maintain the combustion efficiency near the levels of the nominal regime. Accordingly, the generator itself should operate at a variable speed which normally requires power electronics converters. In this paper, we are exploring a new generator concept that uses a stator rotating in opposite direction to the rotor such as the relative velocity between the two components remains constant when diesel engine slows down. The stator itself is driven by a compensator synchronous motor (CM) such as the relative velocity of the rotor is constant, eliminating as such sophisticated power electronics. The model developed for the synchronous machine with a rotating stator is based on Park’s transformation. This new concept was modelled using MATLAB software. Experimental analysis has been conducted using a 500-kW diesel GENSET equipped with a permanent magnet synchronous generator (PMSG). The numerical and experimental results are in good agreement and demonstrate that fuel consumption is reduced with a rotating-mode stator for PMSG during low electrical loads.

New Strategy of Collaborative Acquisition for Connected GNSS Receivers in Deep Urban Environments

Collaborative Positioning (CP) is a better localization technique used to locate a user in challenged environments, which is driven by the increasing presence of cellular phones and mobile devices in urban areas. The basic idea is that the mobile devices can cooperate with each other to improve their ability to determine their position. In this concept, a network of GNSS (Global Navigation Satellite System) receivers can collectively receive available satellite signals, and each receiver can receive signal measurements from other receivers via a communication link. This work shows how to use the Collective Detection (CD) approach to deal with the concept of collaborative or cooperative positioning. Specifically, this paper develops a new strategy allowing a receiver in deep urban environment to locate using the CD approach, while overcoming the implementation complexity problem. The idea consists in applying the CD approach in the case of multiple GNSS receivers to assist a receiver in a difficult situation. A typical case of two connected receivers assisting a receiver in difficulty in a deep urban area shows the effectiveness of this strategy. This strategy is tested with real GNSS signals to analyze its feasibility. The overall gain in complexity can reach up to 46% of what has been achieved in previous works.

Multiperspective Representation of Internal Controls in Business Processes

The internal control process, which is designed to help an organization accomplish specific control objectives, is one of the most important processes, as it can determine whether or not the organization is in compliance with its internal or external requirements. Internal controls emerge from different perspectives. Currently, experts view and act on one control perspective at a time, which creates inefficiencies and duplication. This software engineering research is aimed at proposing a multiperspective framework for representing internal controls, in order to obtain a centralized and comprehensive view of all internal control mechanisms. To carry out this research, we also needed to represent the many different stakeholder perspectives of internal controls. Based on a literature review of mathematical and psychological analysis, we searched for the most suitable multiperspective representation of internal controls, and assessed the many representation options using the AHP (analytical hierarchical process) sensitivity analysis approach. This approach has been applied to a study group which has been called to answer to a questionnaire.

Evaluation of Dynamic Modulus of HMA Sigmoidal Prediction Models and Optimization by Approach of U.S. Mesh Sieve by AFNOR and LC Mesh Sieve

Pavement design tools are not universal. Indeed, in the sizing of pavements in the USA, the prediction models used in the calculation of the dynamic modulus of HMA are not adapted to the characterization of the mineral skeleton of the HMA mix designed with the French method. This article aims to assess the predictive models of the dynamic modulus used in the mechanistic-empirical design for their use in the design of bituminous pavements, and to develop new predictive models taking into account the sieve series LC and AFNOR standards. A total of six types of mixtures were subjected to the determination of complex modulus testing by direct tensile-compression on cylindrical specimens (26-700 LC) over a temperature range (5) and frequency (5) data. Dynamic modulus prediction models |E*| are studied Witczak model 1999 and model Witczak 2006. These models do not take into account the AFNOR or LC mesh sieve, an approach was made in relation to the US mesh sieve to replace ρ200 (0.075 mm), ρ4 (4.76 mm), ρ38 (9.5 mm) and ρ34 (19 mm) respectively by the AFNOR mesh P0.08 (0.08 mm), R5 (5 mm), R10 (10 mm) and R14 (14 mm). The result is the production of two models whose are evaluated by correlation with the values |E*| of modulus measured in the laboratory is satisfactory (R2 = 0.83 respectively R2 = 0.71 and p-value = 0.00). The optimization of these approximate models gave new models with the same frame as the original models and a better correlation with the data observed in the laboratory (respectively R2 = 0. 95 and R2 = 0.91 p-value = 0.00).

Development of a Dynamic Modulus Prediction Model for Hot Mixture Asphalt and Study of the Impact of Aggregate Type and Its Electrochemical Properties

The most famous model known in prediction of dynamic modulus for asphalt concretes is the Witczak and Hirsh models. These models didn’t use the mineralogical and chemical properties of aggregates. Witczak models used the passing or refusal percentage to sieve diameters and Hirsh model used the volumetric analysis. All models developed until now considered that the aggregates were geotechnical conforming to standards. In this study the first mineralogical and chemical properties were considered through the percentage of silica in the rock source of aggregates and the electric aggregate particles charge zeta. Dynamic modulus values used for regression process are determined from complex modulus test on nine asphalt concretes mix designed with aggregate types (basalt of Diack, quartzite of Bakel and Limestone of Bandia). Between Twelve initial inputs, the statistical regression by exclusion process keeps only seven parameters as input for the model. The mineralogical model showed good accuracy with R2 equal to 0.09. The student test on the model parameters showed that all the parameters included in the model were meaningful with good p inferior to 0.05. The Fisher test on the model showed the same result. The analysis of the sensitivity of the mineralogical model to zeta potential showed that the dynamic modulus increases with the positive zeta-potentials and decreases with the negative zeta-potentials. The analysis of the sensitivity of the mineralogical model to the silica showed that the dynamic modulus decreases with the increase of the silica.

Impact of Intrinsic Properties of Aggregate and Volumetric Properties of Hot Mixture Asphalt (HMA) in the Influence of the Resistance to Rutting

Several studies show that properties of Hot Mixture Asphalt (HMA) mix design materials, aggregate gradation and volumetric properties had an influence on their resistance to rutting. However, these properties do not impact in the same way this performance. For a given aggregate type, an infinity aggregate gradation type can be observed, and for each type of HMA several types of bituminous binder can be used. This article aims to measure the evolution of resistance to rutting according to the three main classes of National Cooperative Highway Research Program (NCHRP) aggregate gradation (dense-graded, fine-graded and coarse-graded). To this end, a study was conducted on the measurement of rutting resistance for eight bituminous mixtures manufactured with two bitumen types and two types of manufacturing aggregates. The results showed that there is a priority order of these different parameters on the influence of the resistance to rutting. This highlights a competition between the properties of aggregate and type of granular skeleton. Indeed, for the same type of aggregate, asphalt binder type first impacts resistance to rutting of the HMA followed by aggregate gradation, volumetric properties of the mix and finally by the angularity of the aggregates. However, this order cannot be fixed and can depend of the intensity of each parameter.

Building Productivity Models for Small Enhancements

Software is in constant evolution and many approaches have been suggested to study software maintenance productivity. This research reports on a process to design and implement a productivity model of legacy software based on the measurement of small functional enhancements using the COSMIC ISO 19761 international standard. Two motivations influence this research: 1) understanding the productivity of the software maintenance process to help manage the cost of maintenance;2) understanding the cost drivers that affect the software maintenance productivity. This research reports on an empirical study of a productivity measurement program implemented in a large banking legacy system.

The Analysis and Proposed Modifications to ISO/IEC 25030—Software Engineering—Software Quality Requirements and Evaluation—Quality Requirements

The quality of the software product is a crucial factor that contributes to its success. Therefore, it is important to specify the right software quality requirements that will establish the basis for desired quality of the final system/software product. There are several known methodologies/ processes that support the specification of the system/software functional requirements starting from the user needs to finally obtain the system requirements that the developers can implement through their development process. System/software quality requirements are interdependent with functional requirements, which means that the system/software quality requirements are meant to be specified in parallel with the latter. The ISO/IEC 25000 [1] SQuaRE series of standards include the standard ISO/IEC 25030—Software engineering—Software Quality Requirements and Evaluation—Quality requirements [2], which has as main goal to help specify software quality requirements. As to date, this standard does not offer clear and concise steps that a software quality engineer could follow in order to specify them. This article presents modifications recommended for ISO/IEC 25030 standard, with, among the others, a new requirements definition process that allows for specifying the system/software quality requirements taking into account the existing published system and software quality model ISO/IEC 25010 [3] as well as all the stake- holders of the project.

Tandem hidden Markov models using deep belief networks for offline handwriting recognition

在文档分析和模式识别领域,自由书写的脱机手写识别是一个非常具有挑战性的研究课题。近年来,为了充分探索隐藏在文档图像中的监督信息,许多研究工作试图将多层感知机以一种混合或串联的形式嵌入隐马尔科夫模型当中。然而,因为多层感知机学习能力的不足,学习到的特征对于后续的识别任务不一定是最优的。在本文中,我们针对自由书写的脱机手写识别提出一种基于深度结构的串联方法。在提出的模型中,深度置信网络被用于学习序列数据的紧致表示,隐马尔科夫模型被用于(子-)词的识别。我们在两个公开的数据集上验证了所提出的模型,这两个数据集是分别基于拉丁和阿拉伯语的RIMES和IFN/ENIT;我们还在Devanagari数据集上验证了所提出的模型,这个数据集是基于印度语的。大量的实验展示了所提出模型的优势,特别是相对于多层感知机-隐马尔科夫模型的串联方法。