3,029 research outputs found
Failure Mechanism of True 2D Granular Flows
Most previous experimental investigations of two-dimensional (2D) granular
column collapses have been conducted using three-dimensional (3D) granular
materials in narrow horizontal channels (i.e., quasi-2D condition). Our recent
research on 2D granular column collapses by using 2D granular materials (i.e.,
aluminum rods) has revealed results that differ markedly from those reported in
the literature. We assume a 2D column with an initial height of h0 and initial
width of d0, a defined as their ratio (a =h0/d0), a final height of h , and
maximum run-out distance of d . The experimental data suggest that for the low
a regime (a <0.65) the ratio of the final height to initial height is 1.
However, for the high a regime (a >0.65), the ratio of a to (d-d0)/d0, h0/h ,
or d/d0 is expressed by power-law relations. In particular, the following
power-function ratios (h0/h=1.42a^2/3 and d/d0=4.30a^0.72) are proposed for
every a >0.65. In contrast, the ratio (d-d0)/d0=3.25a^0.96 only holds for 0.65<
a1.5. In addition,
the influence of ground contact surfaces (hard or soft beds) on the final
run-out distance and destruction zone of the granular column under true 2D
conditions is investigated.Comment: 8 page
Structure-specified H∞ loop shaping control for balancing of bicycle robots: A particle swarm optimization approach
In this paper, the particle swarm optimization (PSO) algorithm was used to design the structure-specified H∞ loop shaping controllers for balancing of bicycle robots. The structure-specified H∞ loop shaping controller design normally leads to a complex optimization problem. PSO is an efficient meta-heuristic search which is used to solve multi-objectives and non-convex optimizations. A model-based systematic procedure for designing the particle swarm optimization-based structure-specified H∞ loop shaping controllers was proposed in this research. The structure of the obtained controllers are therefore simpler. The simulation and experimental results showed that the robustness and efficiency of the proposed controllers was gained when compared with the proportional plus derivative (PD) as well as conventional H∞ loop shaping controller. The simulation results also showed a better efficiency of the developed control algorithm compared to the Genetic Algorithm based one
Opposition-based learning for self-adaptive control parameters in differential evolution for optimal mechanism design
In recent decades, new optimization algorithms have attracted much attention from researchers in both gradient- and evolution-based optimal methods. Many strategy techniques are employed to enhance the effectiveness of optimal methods. One of the newest techniques is opposition-based learning (OBL), which shows more power in enhancing various optimization methods. This research presents a new edition of the Differential Evolution (DE) algorithm in which the OBL technique is applied to investigate the opposite point of each candidate of self-adaptive control parameters. In comparison with conventional optimal methods, the proposed method is used to solve benchmark-test optimal problems and applied to real optimizations. Simulation results show the effectiveness and improvement compared with some reference methodologies in terms of the convergence speed and stability of optimal results. © 2019 The Japan Society of Mechanical Engineer
Influence of weather conditions on the quality of ‘Ingrid Marie’ apples and their susceptibility to grey mould infection
Apple (Malus domestica) is one of the most popular fruits consumed around the world. Environmental factors influence the development and quality of apples. We determined the influence of weather conditions on the quality of ‘Ingrid Marie’ apples harvested from eight different orchards in south Sweden in the years 2015–2017 and their susceptibility to infection by grey mould (Botrytis cinerea). We infected apples and collected data on fruit firmness, starch index, weight of fruit and lesion size in addition to collecting data on temperature, rainfall, sunlight and humidity in the period April–September. High rainfall in early April, during tree flowering, and in early June, during early fruit development, correlated with improved quality, namely reduced lesion size and low firmness level. Furthermore, with humidity higher than 77% in early June apples became more tolerant to grey mould, while low temperatures and high humidity in a period from the end of August to end of September, during the end of the fruit cell enlargement stage, correlated with larger apples. We conclude that rainfall, humidity and temperature are important weather factors influencing the quality of apples and their susceptibility to grey mould. This information may help apple growers understand the effects of weather conditions on apples more in detail. From such updated information, preharvest techniques may be applied (e.g. pruning, nutrition, irrigation or drainage) to improve conditions and apple quality as well as to reduce their susceptibility to pathogen attack
Accounting conservatism and banking expertise on board of directors
Previous studies show mixed evidence of the role of banking expertise on the board of directors on accounting conservatism. In this paper, we add to this growing literature by providing an innovative way to measure banking expertise based on life-time working history in banks of all individual directors on the board. We find that accounting conservatism is negatively affected by banking expertise on the board. Also, the results indicate that banking expertise on the board has a more pronounced impact on accounting conservatism when firms have high bankruptcy risk and when firms have high financial leverage. The evidence has some implications for boards of directors
Implementation of Health Policy on Establishment of Provincial Center of Diseases Control (CDC) in Vietnam
Purpose: There is increasing interest in the understanding of key influences over successful implementation of health policies within ever-changing contexts of national health systems. The epidemiological transition in Vietnam, combined with an urgent need for improving efficiency of the national health system under the government’s administrative reforms, form important facilitators of restructuring the public health system. This paper explored the implementation processes of policy on establishment of the Centers for Diseases Control (CDC) in Vietnam during 2016– 2019.
Methods: The study employed a cross-sectional and mixed methods design. Staff surveys were collected at 55 out of 63 provinces and in-depth interviews, focus groups were conducted in three purposefully selected provinces. Quantitative data were analysed using descriptive statistics and qualitative data were analyzed thematically. The innovation implementation framework guided the study.
Results: After 3 years of introduction, 82.5% of provinces had established the CDCs. Implementation of CDC establishment policy was influenced by 1) management support; 2) alignment between policy and practice; 3) values towards CDC,; and 4) implementation climate. Other external key influencers included political, social, and cultural factors.
Conclusion: Our study provides a framework and evidence to guide future inquiry into the factors that affect the relationship between policy implementation and other contextual factors in healthcare organizations
Altitude-Compensating Nozzle (ACN) Project: Planning for Dual-Bell Rocket Nozzle Flight Testing on the NASA F-15B
For more than a half-century, several types of altitude-compensating nozzles have been proposed and analyzed, but very few have been adequately tested in a relevant flight environment. One type of altitude-compensating nozzle is the dual-bell rocket nozzle, which was first introduced into literature in 1949. Although the dual-bell rocket nozzle has been thoroughly studied, this nozzle has still not been tested in a relevant flight environment. This poster presents the top-level rationale and preliminary plans for conducting flight research with the dual-bell rocket nozzle, while exhausting the plume into the freestream flow field at various altitudes. The primary objective is to gain a greater understanding of the nozzle plume sensitivity to freestream flight effects, which will also include detailed measurements of the plume mode transition within the nozzle. To accomplish this goal, the NASA F-15B is proposed as the testbed for advancing the technology readiness level of this greatly-needed capability. All proposed tests include the quantitative performance analysis of the dual-bell rocket nozzle as compared with the conventional-bell nozzle
Data-driven structural health monitoring using feature fusion and hybrid deep learning
Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM
Hierarchical monitoring of people\u27s behaviors in complex environments using multiple cameras
We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people\u27s behaviors at different levels of details using multiple cameras in a large and complex indoor environment.<br /
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