Finite Element Analysis of Contribution of Adhesion and Hysteresis to Shoe-floor Friction

Slips and falls are one of the leading causes of occupational accidents. Understanding the important factors that affect shoe-floor friction is vital for identifying unsafe surfaces and designing better footwear and flooring. While the shoe-floor coefficient of friction is known to be dependent on several factors including shoe and floor roughness, shoe speed, shoe material, and normal load, the mechanisms that cause these effects are not very well understood. The objective of this thesis is to develop a finite element model that simulates the microscopic asperity interaction between shoe and floor surfaces and apply it to quantify the effect of shoe material, topography, loading and sliding speed on shoe-floor adhesion and hysteresis friction. Recent studies have concluded that boundary lubrication is highly pertinent to slipping and that adhesion and hysteresis are the main friction components in boundary lubrication. To have a better knowledge about the mechanisms governing the boundary lubrication friction at the microscopic asperity interaction level, a three dimensional computational model of two rough surfaces is developed which calculates the friction force due to hysteresis and real area of contact (which is proportional to adhesion friction). The computer model includes two rough surfaces of rubber and rigid material. A viscoelastic material model based on parameters calculated from experiments is used to simulate the shoe material. In addition, surface to surface contact algorithm is used for simulating the interaction of the two rough surfaces. The results show that microscopic shoe and floor roughness, followed by material properties, shoe sliding speed, and normal loading affect hysteresis and adhesion coefficient of friction. The model provides an improved insight about the mechanisms that cause changes in adhesion and hysteresis when altering shoe and floor roughness, sliding speed, shoe material and normal loading and it can be useful in development of slip resistant shoes and floorings

A mixed-signal feed-forward neural network architecture with on-chip learning in CMOS 0.18 microns.

One of the main characteristics of the neural networks is their high number of interconnections between the neurons through synaptic multipliers. Interconnections occupy large area and increase the circuit complexity which limits the size of the fully parallel network. To implement large size networks, time-multiplexing should be used. Two new mixed-signal time-multiplexed architectures are proposed for on-chip mixed-signal neural networks. MRIII is used for training the network which is more robust to mixed-signal designs. The problem of node addressing and routing is solved by performing the operations in current mode. The architectures are simple and compact and learning is performed on-chip without the host computer, which reduces the cost of learning for the network. Mixed-signal MDACs are used for synaptic multiplication. A new compact architecture is proposed for the MDAC to reduce the area, power consumption and noise. The proposed MDAC performs the digital to analog conversion in series. Comparison shows that the new MDAC is more linear and has less noise than the conventional MDAC. The layout of the proposed MDAC is relatively easy, since it has a repetitive structure. For the first time, a new 12-bit MDAC is implemented, which enables us to perform on-chip training. The proposed 12-bit MDAC still occupies less area compared to the 7-bit conventional MDAC. A new low-voltage class-AB high-drive buffer for driving the voltages off-chip is developed. The proposed buffer is able to drive capacitive loads up to 2nF. It also drives resistive loads down to 2kO from rail to rail. For compensation, a 0.2pF capacitor is used.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .M47. Source: Masters Abstracts International, Volume: 42-01, page: 0298. Adviser: M. Ahmadi. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003

A Ground Robot for Search And Rescue in Hostile Environment

The recent sheer developments in the field of robotics has encouraged the researcher to consider the robots assisting human in different aspects of life. In this context, search and rescue is a very interesting ambient where the capabilities offered by the robots can be used to not only augment the quality of service but also impose lower risk to the human members of the rescue team. To this purpose, project SHERPA has been defined to investigate an intelligent heterogeneous robotic team in a search and rescue mission. The robotic team includes flying robots such as fixed wing and quad copters for the purpose of patrolling and surveillance and a ground rover that is mainly considered to provide a mobile power replenishment service for the quadrotors. Navigation of the ground rover on the unstructured outdoor environment defined by the SHERPA is of the main focuses of this thesis. Due to roughness of the terrain, there are a lot of issues on the way of a successful localization. Moreover, the planning has to be compatible with the robot and environment constraints to avoid imposing a risk of mechanical damage to the system. To accomplish the battery exchange operation, the rover is equipped with two auxiliary devices namely "Sherpa box" and "Sherpa robotic arm". In this thesis, firstly, designs of the two devices are introduced to the reader in details. Secondly, their integration with the ground rover will be covered. Finally two important benchmarks of the SHERPA project, namely "human leashing" and "battery exchange operation", will be addressed

Computational Models for Predicting Shoe Friction and Wear

Slips and falls are a serious occupational and health problem. Insufficient friction between a shoe and flooring, quantified by the coefficient of friction (COF), increases the likelihood of slips and falls. Moreover, shoe’s slip-resistant properties change over its lifetime due to wear. This dissertation applies physics-based computational finite element modeling techniques to predict shoe-floor-contaminant friction. Computational models that simulate COF due to hysteresis are developed using multiscale methods. These models are used to assess the effects of shoe design factors and biomechanical parameters of human gait on the predicted COF. To address a gap in the literature regarding models that simulate shoe wear progression, this dissertation develops and validates an innovative finite element modeling process utilizing Archard’s law that predicts shoe wear. Models introduced in this dissertation not only increase the understanding of slips and falls but also offer a valuable tool that can be used in designing slip-resistant and durable shoes in order to achieve the ultimate goal of reducing slip and fall injuries

Enabling emergency flow prioritization in SDN networks

Emergency services must be able to transfer data with high priority over different networks. With 5G, slicing concepts at mobile network connections are introduced, allowing operators to divide portions of their network for specific use cases. In addition, Software-Defined Networking (SDN) principles allow to assign different Quality-of-Service (QoS) levels to different network slices.This paper proposes an SDN-based solution, executable both offline and online, that guarantees the required bandwidth for the emergency flows and maximizes the best-effort flows over the remaining bandwidth based on their priority. The offline model allows to optimize the problem for a batch of flow requests, but is computationally expensive, especially the variant where flows can be split up over parallel paths. For practical, dynamic situations, an online approach is proposed that periodically recalculates the optimal solution for all requested flows, while using shortest path routing and a greedy heuristic for bandwidth allocation for the intermediate flows.Afterwards, the offline approaches are evaluated through simulations while the online approach is validated through physical experiments with SDN switches, both in a scenario with 500 best-effort and 50 emergency flows. The results show that the offline algorithm is able to guarantee the resource allocation for the emergency flows while optimizing the best-effort flows with a sub-second execution time. As a proof-of-concept, a physical setup with Zodiac switches effectively validates the feasibility of the online approach in a realistic setup

Bioengineering of Antibody Fragments: Challenges and Opportunities.

Antibody fragments are used in the clinic as important therapeutic proteins for treatment of indications where better tissue penetration and less immunogenic molecules are needed. Several expression platforms have been employed for the production of these recombinant proteins, from which E. coli and CHO cell-based systems have emerged as the most promising hosts for higher expression. Because antibody fragments such as Fabs and scFvs are smaller than traditional antibody structures and do not require specific patterns of glycosylation decoration for therapeutic efficacy, it is possible to express them in systems with reduced post-translational modification capacity and high expression yield, for example, in plant and insect cell-based systems. In this review, we describe different bioengineering technologies along with their opportunities and difficulties to manufacture antibody fragments with consideration of stability, efficacy and safety for humans. There is still potential for a new production technology with a view of being simple, fast and cost-effective while maintaining the stability and efficacy of biotherapeutic fragments

Advances and challenges in grid tied photovoltaic systems

Photovoltaic (PV) technology is gathering momentum around the word. Global PV energy harvest has been more than doubled since 2010. Grid connected PV (GCPV) systems can be found in different scales classified into three categories of small scale, medium scale and utility scale. Considering size of the system various configurations are suggested for the GCPV systems while each configuration might be assessed by factors such as efficiency, reliability, expandability and cost. Moreover, high integration of GCPV systems into the power system network creates several technical problems mostly coming from the intermittent nature of solar energy. In addition, to achieve a higher degree of power system reliability, GCPV systems are required to support the grid in abnormal condition such a faults and deviation from standard frequency. This paper provides a comprehensive review on GCPV systems. Various configuration proposed by the literature will be discussed. Cost study and impact of technical and environmental factors on the total expense and revenue of GCPV installation will be investigated. Different aspects of PV integration into the power network will be discussed. Problem and solutions will be studied as well. Finally grid requirements and active and reactive power support will be reviewed. (C) 2015 Elsevier Ltd. All rights reserved

Adopting GRASP to solve a novel model for bus timetabling problem with minimum transfer and fruitless waiting times

This paper addresses a variant of bus timetabling problem assuming that travel times changes dynamically over the planning horizon. In addition to minimizing the transfer waiting time, another objective, namely minimizing the fruitless waiting time, is introduced in this paper as a new realistic objective. First, the problem is formulated as a mixed integer linear programming model. Then, since commercial solvers become inefficient to solve moderate and large sized instances of the problem (due to the NP-hardness), a GRASP heuristic algorithm is developed. Computational experiments over a variety of random instances verify the performance of the proposed method

Age Estimation Based on Children’s Voice: A Fuzzy-Based Decision Fusion Strategy

Automatic estimation of a speaker’s age is a challenging research topic in the area of speech analysis. In this paper, a novel approach to estimate a speaker’s age is presented. The method features a “divide and conquer” strategy wherein the speech data are divided into six groups based on the vowel classes. There are two reasons behind this strategy. First, reduction in the complicated distribution of the processing data improves the classifier’s learning performance. Second, different vowel classes contain complementary information for age estimation. Mel-frequency cepstral coefficients are computed for each group and single layer feed-forward neural networks based on self-adaptive extreme learning machine are applied to the features to make a primary decision. Subsequently, fuzzy data fusion is employed to provide an overall decision by aggregating the classifier’s outputs. The results are then compared with a number of state-of-the-art age estimation methods. Experiments conducted based on six age groups including children aged between 7 and 12 years revealed that fuzzy fusion of the classifier’s outputs resulted in considerable improvement of up to 53.33% in age estimation accuracy. Moreover, the fuzzy fusion of decisions aggregated the complementary information of a speaker’s age from various speech sources