10,411 research outputs found
On Frankl and Furedi's conjecture for 3-uniform hypergraphs
The Lagrangian of a hypergraph has been a useful tool in hypergraph extremal
problems. In most applications, we need an upper bound for the Lagrangian of a
hypergraph. Frankl and Furedi in \cite{FF} conjectured that the -graph with
edges formed by taking the first sets in the colex ordering of
has the largest Lagrangian of all -graphs with
edges. In this paper, we give some partial results for this conjecture.Comment: 19 pages, 1 figure. arXiv admin note: substantial text overlap with
arXiv:1211.650
The Comparison of Composite Aircraft Field Repair Method (CAFRM) with Traditional Aircraft Repair Technologies
In the aviation industry, manufacturers made the transition from aluminum to composite materials for the majority of their primary structures over the last few decades. While the design and manufacturing techniques have consistently evolved, field repair methods were consistently overlooked. In this study, specimens fabricated using some of the common repair methods such as the autoclave repair method, and Double Vacuum Debulk (DVD) repair method were tested against the Composite Aircraft Field Repair Method (CAFRM) proposed by the researcher. Specimens were tested with microscopy, acid digestion, short beam shear, and mode I fracture tests. The researcher was able to determine the specimen\u27s void content, fiber volume fraction, shear strength, and opening mode interlaminar fracture toughness for the specimens fabricated using the different repair methods. The specimens fabricated using the autoclave repair method, DVD repair method, and CAFRM showed significant differences in void content, shear strength, and opening mode interlaminar fracture toughness. However, there were no significant differences between the specimens for fiber volume fraction
The Effects of Adding Attachments in Conventional Composite Hybrid Joints on Tensile Strength
Mechanical fasteners are commonly used in today’s composite aircraft adhesive joints. The primary purpose for using mechanical fasteners was to provide redundancy to the adhesive joints because of the uncertainties associated with an adhesive only joint. Therefore, the use of a fastener in a conventional composite hybrid joint was mainly a part of a fail-safe design. Acting as a safety backup, the fastener did not contribute tensile strength to the joint until the adhesive joint failed. When only being used for redundancy purposes in case of an adhesive joint failure, the existing fastener became simply an added weight to the aircraft structure. In this research, a proposed new design was incorporated into the composite hybrid joint where two different types of attachments were used in order to provide alternate load paths to redirect load to the fastener and utilize the fastener to provide strength to the joint once the joint was loaded. Two types of attachments were used: a stepped attachment and a curved attachment. The attachments would reduce the amount of load induced in the adhesive joint and increase the overall strength of the hybrid joint. Experiments were conducted with both the conventional hybrid joint and the new hybrid joint design with two different attachments to assess effectiveness of the new design, the more efficient attachment type, and whether the added weight of the attachments could be justified by the improvements in the overall strength of the joint. Each type of hybrid joints consisted of 15 specimens. A total of 45 specimens were prepared for this study. The specimens were tested with a MTS Systems Corporation testing apparatus with a 22 kip (±100kN) load cell. The ultimate tensile load data gathered were used to compare the strength of the different hybrid joints. From the tensile testing data collected of the three different types of specimens, the hybrid joints with attachments showed a significant improvement in ultimate tensile strength compared to the conventional hybrid joints. Conventional hybrid joints had an average ultimate tensile strength of 5859.99 lbf. Hybrid joints with curved attachments had an average ultimate tensile strength of 10617.62 lbf, which was 81.19% higher than the conventional hybrid joints. Hybrid joints with stepped attachments had an average ultimate tensile strength of 10342.14 lbf which was 76.49% higher than conventional hybrid joints. Hybrid joints with curved attachments were also found to be more efficient compared to hybrid joints with stepped attachments and improved the ultimate tensile strength 2.66%
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
International aviation collaboration in a study abroad program
Aviation is a global industry. As we continue to push the boundaries of technical advancements and operational efficiency in the aviation industry, the reliance on the global workforce will continue to increase. In the United States of America, the majority of educational establishments focus mainly in the education and training of students for the US aviation industry. The narrow focus significantly limits the amount of exposure and opportunities available to the students of these educational establishments. In order to improve global exposure and provide students with opportunities outside of the US aviation industry, the authors of the paper successfully established a study abroad program, allowing students to explore and understand European culture, history, and its\u27 aviation industry. Furthermore, students enrolled in the program had the opportunity to work with fellow aviation students from two different European universities on a joint research project. Students were required to complete homework assignments during the program designed to increase global awareness and cultural understanding. Feedback from students was positive in both written and scale questions
New insights into the mechanism of low high-density lipoprotein cholesterol in obesity
Obesity, a significant risk factor for various chronic diseases, is universally related to dyslipidemia mainly represented by decreasing high-density lipoprotein cholesterol (HDL-C), which plays an indispensible role in development of cardiovascular disease (CVD). However, the mechanisms underlying obesity and low HDL-C have not been fully elucidated. Previous studies have focused on the alteration of HDL catabolism in circulation following elevated triglyceride (TG). But recent findings suggested that liver and fat tissue played pivotal role in obesity related low HDL-C. Some new molecular pathways like microRNA have also been proposed in the regulation of HDL metabolism in obesity. This article will review recent advances in understanding of the potential mechanism of low HDL-C in obesity
Modeling relation paths for knowledge base completion via joint adversarial training
Knowledge Base Completion (KBC), which aims at determining the missing
relations between entity pairs, has received increasing attention in recent
years. Most existing KBC methods focus on either embedding the Knowledge Base
(KB) into a specific semantic space or leveraging the joint probability of
Random Walks (RWs) on multi-hop paths. Only a few unified models take both
semantic and path-related features into consideration with adequacy. In this
paper, we propose a novel method to explore the intrinsic relationship between
the single relation (i.e. 1-hop path) and multi-hop paths between paired
entities. We use Hierarchical Attention Networks (HANs) to select important
relations in multi-hop paths and encode them into low-dimensional vectors. By
treating relations and multi-hop paths as two different input sources, we use a
feature extractor, which is shared by two downstream components (i.e. relation
classifier and source discriminator), to capture shared/similar information
between them. By joint adversarial training, we encourage our model to extract
features from the multi-hop paths which are representative for relation
completion. We apply the trained model (except for the source discriminator) to
several large-scale KBs for relation completion. Experimental results show that
our method outperforms existing path information-based approaches. Since each
sub-module of our model can be well interpreted, our model can be applied to a
large number of relation learning tasks.Comment: Accepted by Knowledge-Based System
Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme
Joint extraction of entities and relations is an important task in
information extraction. To tackle this problem, we firstly propose a novel
tagging scheme that can convert the joint extraction task to a tagging problem.
Then, based on our tagging scheme, we study different end-to-end models to
extract entities and their relations directly, without identifying entities and
relations separately. We conduct experiments on a public dataset produced by
distant supervision method and the experimental results show that the tagging
based methods are better than most of the existing pipelined and joint learning
methods. What's more, the end-to-end model proposed in this paper, achieves the
best results on the public dataset
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