A Robust Solution to Strengthening Bridges

2009 ◽  
Author(s):  
J.S. Lane ◽  
M.B. Leeming ◽  
P.S. Fashole-Luke
Keyword(s):  
Robust Solution

scholarly journals Robust Step Detection from Different Waist-Worn Sensor Positions: Implications for Clinical Studies

2020 ◽  
Vol 4 (1) ◽  
pp. 50-58
Author(s):  
Matthias  Tietsch ◽  
Amir Muaremi ◽  
Ieuan Clay ◽  
Felix Kluge ◽  
Holger Hoefling ◽  
...  
Keyword(s):  
Clinical Study ◽  
Clinical Studies ◽  
Neurological Diseases ◽  
Human Gait ◽  
Robust Solution ◽  
Step Detection ◽  
Study Protocols ◽  
Study Results ◽  
Wide Range ◽  
The Impact

Analyzing human gait with inertial sensors provides valuable insights into a wide range of health impairments, including many musculoskeletal and neurological diseases. A representative and reliable assessment of gait requires continuous monitoring over long periods and ideally takes place in the subjects’ habitual environment (real-world). An inconsistent sensor wearing position can affect gait characterization and influence clinical study results, thus clinical study protocols are typically highly proscriptive, instructing all participants to wear the sensor in a uniform manner. This restrictive approach improves data quality but reduces overall adherence. In this work, we analyze the impact of altering the sensor wearing position around the waist on sensor signal and step detection. We demonstrate that an asymmetrically worn sensor leads to additional odd-harmonic frequency components in the frequency spectrum. We propose a robust solution for step detection based on autocorrelation to overcome sensor position variation (sensitivity = 0.99, precision = 0.99). The proposed solution reduces the impact of inconsistent sensor positioning on gait characterization in clinical studies, thus providing more flexibility to protocol implementation and more freedom to participants to wear the sensor in the position most comfortable to them. This work is a first step towards truly position-agnostic gait assessment in clinical settings.

scholarly journals Many Ways Lead to the Goal—Possibilities of Autonomous and Infrastructure-Based Indoor Positioning

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 397
Author(s):  
Hossein Shoushtari ◽  
Thomas Willemsen ◽  
Harald Sternberg
Keyword(s):  
Mobile Networks ◽  
Dead Reckoning ◽  
Simulated Data ◽  
Minimum Cost ◽  
Cost Effective ◽  
Satellite System ◽  
Position Estimation ◽  
Robust Solution ◽  
Position Data ◽  
Global Navigation Satellite

There are many ways to navigate in Global Navigation Satellite System-(GNSS) shaded areas. Reliable indoor pedestrian navigation has been a central aim of technology researchers in recent years; however, there still exist open challenges requiring re-examination and evaluation. In this paper, a novel dataset is used to evaluate common approaches for autonomous and infrastructure-based positioning methods. The autonomous variant is the most cost-effective realization; however, realizations using the real test data demonstrate that the use of only autonomous solutions cannot always provide a robust solution. Therefore, correction through the use of infrastructure-based position estimation based on smartphone technology is discussed. This approach invokes the minimum cost when using existing infrastructure, whereby Pedestrian Dead Reckoning (PDR) forms the basis of the autonomous position estimation. Realizations with Particle Filters (PF) and a topological approach are presented and discussed. Floor plans and routing graphs are used, in this case, to support PDR positioning. The results show that the positioning model loses stability after a given period of time. Fifth Generation (5G) mobile networks can enable this feature, as well as a massive number of use-cases, which would benefit from user position data. Therefore, a fusion concept of PDR and 5G is presented, the benefit of which is demonstrated using the simulated data. Subsequently, the first implementation of PDR with 5G positioning using PF is carried out.

Peripheral Facial Features Guiding Eye Movements and Reducing Fixational Variability

2021 ◽  
Author(s):  
Nicole X Han ◽  
Puneeth N. Chakravarthula ◽  
Miguel P. Eckstein
Keyword(s):  
Eye Movements ◽  
Spatial Configuration ◽  
Oculomotor System ◽  
Facial Features ◽  
Robust Solution ◽  
The Face ◽  
And Gender ◽  
Social Importance ◽  
Fixation Variability

Face processing is a fast and efficient process due to its evolutionary and social importance. A majority of people direct their first eye movement to a featureless point just below the eyes that maximizes accuracy in recognizing a person's identity and gender. Yet, the exact properties or features of the face that guide the first eye movements and reduce fixational variability are unknown. Here, we manipulated the presence of the facial features and the spatial configuration of features to investigate their effect on the location and variability of first and second fixations to peripherally presented faces. Results showed that observers can utilize the face outline, individual facial features, and feature spatial configuration to guide the first eye movements to their preferred point of fixation. The eyes have a preferential role in guiding the first eye movements and reducing fixation variability. Eliminating the eyes or altering their position had the greatest influence on the location and variability of fixations and resulted in the largest detriment to face identification performance. The other internal features (nose and mouth) also contribute to reducing fixation variability. A subsequent experiment measuring detection of single features showed that the eyes have the highest detectability (relative to other features) in the visual periphery providing a strong sensory signal to guide the oculomotor system. Together, the results suggest a flexible multiple-cue approach that might be a robust solution to cope with how the varying eccentricities in the real world influence the ability to resolve individual feature properties and the preferential role of the eyes.

Build Time Analysis of Additive Manufacturing for Next Generation SLS Systems

2020 ◽  
Author(s):  
Michael Machado ◽  
Raul Fangueiro ◽  
Daniel Barros ◽  
Luís Nobre ◽  
João Bessa ◽  
...  
Keyword(s):  
Additive Manufacturing ◽  
Selective Laser Sintering ◽  
Material Point ◽  
Point Of View ◽  
Efficient Production ◽  
Robust Solution ◽  
Functional Product ◽  
Build Time ◽  
Consistent Solution ◽  
Production Solutions

Abstract With the recent advances in the additive manufacturing (AM) production technologies, AM is becoming more common in today’s industry, nowadays is a normal practice to use this solution either to test a new prototype or to manufacture a functional product. The increase application is mainly due to significant developments in the production solutions of the AM. These recent developments are resulting in an increase search for new and more efficient production solutions. This search is always focused in producing more efficiently, with a greater variety of materials and produce part with better quality and proprieties. From an industrial point of view, one of the types of additive manufacturing that is increasing the percentage of use is the selective laser sintering (SLS) technologies. Although this process was first used in the mid-80’s, it has shown great developments in the recent years. This evolution of the process allowed it to become a solid solution even if it is highly time consuming, especially when compared with other types of addictive manufacturing. From the several aspects that make the SLS a robust solution is the fact that it offers a consistent solution to produce high complex part with good mechanical properties, and also the ability to use many core materials, from polymers, metal alloy, ceramics or even composites materials. Due to the fact that the production of part using SLS technologies takes a long time, shows the relevance to study the entire process in order to quantify the time spent in each stage a very important step. This study can be conducted with two major goals, in one hand to be able to predict the build time needed to complete a predetermined task, and in other hand, to improve the overall efficiency of the process based on the knowledge acquired in the previous analysis. These two aspects are important because they allow the machine operator to choose the production plan more carefully and also to know all the parameters of the process to make it more efficient. In this paper will be presented a survey of the major stages of a SLS process in order to quantify the time consumed in each one of the stages, and if possible, determine solution to reduce the time spent. To better understand the topic the paper will be divided according to the proprieties and time consumed in each of the elements of the process. In other words, it will be divided accordingly to a machine, laser and material point of view. Furthermore, this paper will be focused in the SLS process and the productions based in a polymeric powder, therefore also containing aspects related to the power source used.

scholarly journals An Empirical Investigation on a Multiple Filters-Based Approach for Remaining Useful Life Prediction

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 35 ◽  
Author(s):  
Hung-Cuong Trinh ◽  
Yung-Keun Kwon
Keyword(s):  
Scoring Function ◽  
Principal Component ◽  
Remaining Useful Life ◽  
Filter Method ◽  
Feature Construction ◽  
Training Set ◽  
Robust Solution ◽  
Benchmark Datasets ◽  
Useful Life ◽  
Better Than

Feature construction is critical in data-driven remaining useful life (RUL) prediction of machinery systems, and most previous studies have attempted to find a best single-filter method. However, there is no best single filter that is appropriate for all machinery systems. In this work, we devise a straightforward but efficient approach for RUL prediction by combining multiple filters and then reducing the dimension through principal component analysis. We apply multilayer perceptron and random forest methods to learn the underlying model. We compare our approach with traditional single-filtering approaches using two benchmark datasets. The former approach is significantly better than the latter in terms of a scoring function with a penalty for late prediction. In particular, we note that selecting a best single filter over the training set is not efficient because of overfitting. Taken together, we validate that our multiple filters-based approach can be a robust solution for RUL prediction of various machinery systems.

scholarly journals Brick Strex: a robust device built of LEGO bricks for mechanical manipulation of cells

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elina Mäntylä ◽  
Teemu O. Ihalainen
Keyword(s):  
Molecular Mechanisms ◽  
Compressive Force ◽  
Tissue Mechanics ◽  
Cell Junctions ◽  
Physical Factors ◽  
Force Transmission ◽  
Cell Nuclei ◽  
Microscopy Imaging ◽  
Robust Solution ◽  
Mechanical Manipulation

AbstractCellular forces, mechanics and other physical factors are important co-regulators of normal cell and tissue physiology. These cues are often misregulated in diseases such as cancer, where altered tissue mechanics contribute to the disease progression. Furthermore, intercellular tensile and compressive force-related signaling is highlighted in collective cell behavior during development. However, the mechanistic understanding on the role of physical forces in regulation of cellular physiology, including gene expression and signaling, is still lacking. This is partly because studies on the molecular mechanisms of force transmission require easily controllable experimental designs. These approaches should enable both easy mechanical manipulation of cells and, importantly, readouts ranging from microscopy imaging to biochemical assays. To achieve a robust solution for mechanical manipulation of cells, we developed devices built of LEGO bricks allowing manual, motorized and/or cyclic cell stretching and compression studies. By using these devices, we show that $$\upbeta$$ β -catenin responds differentially to epithelial monolayer stretching and lateral compression, either localizing more to the cell nuclei or cell–cell junctions, respectively. In addition, we show that epithelial compression drives cytoplasmic retention and phosphorylation of transcription coregulator YAP1. We provide a complete part listing and video assembly instructions, allowing other researchers to build and use the devices in cellular mechanics-related studies.

scholarly journals Investigation on Die Crack in a Stacked Die Package Using Finite Element Analysis

2021 ◽  
pp. 83-91
Author(s):  
Jefferson Talledo
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Coefficient Of Thermal Expansion ◽  
Crack Problem ◽  
Element Analysis ◽  
Robust Solution ◽  
Die Attach ◽  
Die Stress ◽  
Cte Mismatch ◽  
Semiconductor Packages

Die crack is one of the problems in stacked die semiconductor packages. As silicon dies become thinner in such packages due to miniaturization requirement, the tendency to have die crack increases. This study presents the investigation done on a die crack issue in a stacked die package using finite element analysis (FEA). The die stress induced during the package assembly processes from die attach to package strip reflow was analyzed and compared with the actual die crack failure in terms of the location of maximum die stress at unit level as well as strip level. Stresses in the die due to coefficient of thermal expansion (CTE) mismatch of the package component materials and mechanical bending of the package in strip format were taken into consideration. Comparison of the die stress with actual die crack pointed to strip bending as the cause of the problem and not CTE mismatch. It was found that the die crack was not due to the thermal processes involved during package assembly. This study showed that analyzing die stress using FEA could help identify the root cause of a die crack problem during the stacked die package assembly and manufacturing as crack occurs at locations of maximum stress. The die crack mechanism can also be understood through FEA simulation and such understanding is very important in coming up with robust solution.

scholarly journals Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Jihyun Kim ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Distribution System ◽  
Short Term Memory ◽  
Electrical Power ◽  
Electricity Consumption ◽  
Classification Performance ◽  
Robust Solution ◽  
Proposed Model ◽  
Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

A general framework of model functions for rapid and robust solution of Rachford–Rice type of equations

2012 ◽  
Vol 322-323 ◽  
pp. 9-18 ◽  
Author(s):  
Vassilis Gaganis ◽  
Dimitris Marinakis ◽  
Nikos Varotsis
Keyword(s):  
General Framework ◽  
Robust Solution

Multi-Objective Robust Optimization Under Interval Uncertainty Using Online Approximation and Constraint Cuts

2011 ◽  
Vol 133 (6) ◽  
Author(s):  
W. Hu ◽  
M. Li ◽  
S. Azarm ◽  
A. Almansoori
Keyword(s):  
Optimization Problem ◽  
Optimization Problems ◽  
Computational Effort ◽  
Interval Uncertainty ◽  
Test Results ◽  
Robust Solution ◽  
Multi Objective ◽  
Function Calls ◽  
Level Problem ◽  
Upper Level

Many engineering optimization problems are multi-objective, constrained and have uncertainty in their inputs. For such problems it is desirable to obtain solutions that are multi-objectively optimum and robust. A robust solution is one that as a result of input uncertainty has variations in its objective and constraint functions which are within an acceptable range. This paper presents a new approximation-assisted MORO (AA-MORO) technique with interval uncertainty. The technique is a significant improvement, in terms of computational effort, over previously reported MORO techniques. AA-MORO includes an upper-level problem that solves a multi-objective optimization problem whose feasible domain is iteratively restricted by constraint cuts determined by a lower-level optimization problem. AA-MORO also includes an online approximation wherein optimal solutions from the upper- and lower-level optimization problems are used to iteratively improve an approximation to the objective and constraint functions. Several examples are used to test the proposed technique. The test results show that the proposed AA-MORO reasonably approximates solutions obtained from previous MORO approaches while its computational effort, in terms of the number of function calls, is significantly reduced compared to the previous approaches.

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