106 research outputs found
Dynamics analysis of the pitch control reducer for MW wind turbine
An analytic dynamics model was presented for the three-stage planetary transmission in the pitch control reducer for MW wind turbine based on the lumped-parameter method. The mechanical characteristic of the contact components was analyzed using the stiffness factor method. All the stiffness sub-matrices were combined to form the overall stiffness matrix of the three-stage transmission. According to the analytic model and the parameters of the pitch control gearbox, the movement differential equations were solved to investigate the natural frequencies and the vibration modes. Then, the undamped and damping forced vibration response were studied. A test rig was set up to measure the vibration displacement of the ring at the second stage and the output shaft under the nominal load condition, the comparison of the analytic forced vibration response with the experimental results validates the effectiveness of the lumped-parameter dynamics model for the pitch control reducer. This paper provides a reference for the dynamics optimization of multistage planetary transmission
Effects of metastasis-associated in colon cancer 1 inhibition by small hairpin RNA on ovarian carcinoma OVCAR-3 cells
<p>Abstract</p> <p>Background</p> <p>Metastasis-associated in colon cancer 1 (MACC1) is demonstrated to be up-regulated in several types of cancer, and can serve as biomarker for cancer invasion and metastasis. To investigate the relations between MACC1 and biological processes of ovarian cancer, MACC1 specific small hairpin RNA (shRNA) expression plasmids were used to investigate the effects of MACC1 inhibition on ovarian carcinoma OVCAR-3 cells.</p> <p>Methods</p> <p>Expressions of MACC1 were detected in different ovarian tissues by immunohistochemistry. MACC1 specific shRNA expression plasmids were constructed and transfected into OVCAR-3 cells. Then, expressions of MACC1 were examined by reverse transcription polymerase chain reaction (RT-PCR) and Western blot. Cell proliferation was observed by MTT and monoplast colony formation assay. Flow cytometry and TUNEL assay were used to measure cell apoptosis. Cell migration was assessed by wound healing and transwell migration assay. Matrigel invasion and xenograft model assay were performed to analyze the potential of cell invasion. Activities of Met, MEK1/2, ERK1/2, Akt, cyclinD1, caspase3 and MMP2 protein were measured by Western blot.</p> <p>Results</p> <p>Overexpressions of MACC1 were detected in ovarian cancer tissues. Expression of MACC1 in OVCAR-3 cells was significantly down-regulated by MACC1 specific small hairpin RNA. In OVCAR-3 cells, down-regulation of MACC1 resulted in significant inhibition of cell proliferation, migration and invasion, meanwhile obvious enhancement of apoptosis. As a consequence of MACC1 knockdown, expressions of Met, p-MEK1/2, p-ERK1/2, cyclinD1 and MMP2 protein decreased, level of cleaved capase3 was increased.</p> <p>Conclusions</p> <p>RNA interference (RNAi) against MACC1 could serve as a promising intervention strategy for gene therapy of ovarian carcinoma, and the antitumor effects of MACC1 knockdown might involve in the inhibition of HGF/Met and MEK/ERK pathways.</p
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation
Explainability poses a major challenge to artificial intelligence (AI)
techniques. Current studies on explainable AI (XAI) lack the efficiency of
extracting global knowledge about the learning task, thus suffer deficiencies
such as imprecise saliency, context-aware absence and vague meaning. In this
paper, we propose the class association embedding (CAE) approach to address
these issues. We employ an encoder-decoder architecture to embed sample
features and separate them into class-related and individual-related style
vectors simultaneously. Recombining the individual-style code of a given sample
with the class-style code of another leads to a synthetic sample with preserved
individual characters but changed class assignment, following a cyclic
adversarial learning strategy. Class association embedding distills the global
class-related features of all instances into a unified domain with well
separation between classes. The transition rules between different classes can
be then extracted and further employed to individual instances. We then propose
an active XAI framework which manipulates the class-style vector of a certain
sample along guided paths towards the counter-classes, resulting in a series of
counter-example synthetic samples with identical individual characters.
Comparing these counterfactual samples with the original ones provides a
global, intuitive illustration to the nature of the classification tasks. We
adopt the framework on medical image classification tasks, which show that more
precise saliency maps with powerful context-aware representation can be
achieved compared with existing methods. Moreover, the disease pathology can be
directly visualized via traversing the paths in the class-style space
N-(4-Chlorobenzylidene)-1-naphthylamine
The title compound, C17H12ClN, represents a trans isomer with respect to the C=N bond; the dihedral angle between the planes of the naphthyl and benzene groups is 66.53 (5)°
TransRepair: Context-aware Program Repair for Compilation Errors
Automatically fixing compilation errors can greatly raise the productivity of
software development, by guiding the novice or AI programmers to write and
debug code. Recently, learning-based program repair has gained extensive
attention and became the state-of-the-art in practice. But it still leaves
plenty of space for improvement. In this paper, we propose an end-to-end
solution TransRepair to locate the error lines and create the correct
substitute for a C program simultaneously. Superior to the counterpart, our
approach takes into account the context of erroneous code and diagnostic
compilation feedback. Then we devise a Transformer-based neural network to
learn the ways of repair from the erroneous code as well as its context and the
diagnostic feedback. To increase the effectiveness of TransRepair, we summarize
5 types and 74 fine-grained sub-types of compilations errors from two
real-world program datasets and the Internet. Then a program corruption
technique is developed to synthesize a large dataset with 1,821,275 erroneous C
programs. Through the extensive experiments, we demonstrate that TransRepair
outperforms the state-of-the-art in both single repair accuracy and full repair
accuracy. Further analysis sheds light on the strengths and weaknesses in the
contemporary solutions for future improvement.Comment: 11 pages, accepted to ASE '2
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
3D anomaly detection is an emerging and vital computer vision task in
industrial manufacturing (IM). Recently many advanced algorithms have been
published, but most of them cannot meet the needs of IM. There are several
disadvantages: i) difficult to deploy on production lines since their
algorithms heavily rely on large pre-trained models; ii) hugely increase
storage overhead due to overuse of memory banks; iii) the inference speed
cannot be achieved in real-time. To overcome these issues, we propose an easy
and deployment-friendly network (called EasyNet) without using pre-trained
models and memory banks: firstly, we design a multi-scale multi-modality
feature encoder-decoder to accurately reconstruct the segmentation maps of
anomalous regions and encourage the interaction between RGB images and depth
images; secondly, we adopt a multi-modality anomaly segmentation network to
achieve a precise anomaly map; thirdly, we propose an attention-based
information entropy fusion module for feature fusion during inference, making
it suitable for real-time deployment. Extensive experiments show that EasyNet
achieves an anomaly detection AUROC of 92.6% without using pre-trained models
and memory banks. In addition, EasyNet is faster than existing methods, with a
high frame rate of 94.55 FPS on a Tesla V100 GPU
1-(2,3-Dimethoxybenzylidene)-2-(2,4-dinitrophenyl)hydrazine
In the title compound, C15H14N4O6, the dihedral angle between the aromatic rings is 3.7 (4)°. The nitro groups make dihedral angles of 6.0 (4) and 5.2 (4)° with the parent ring and are oriented at 6.0 (6)° with respect to each other. The methoxy groups are inclined at 54.0 (2) and 2.5 (3)° with respect to the benzene ring to which they are attached. In the crystal, molecules are linked by weak C—H⋯O interactions. The molecular conformation is consolidated by an intramolecular N—H⋯O hydrogen bond
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for
identifying the defects of advancing machining and precision manufacturing.
Despite some methodological advances in this area, the scarcity of datasets and
the lack of a systematic benchmark hinder its development. We introduce
Real3D-AD, a challenging high-precision point cloud anomaly detection dataset,
addressing the limitations in the field. With 1,254 high-resolution 3D items
from forty thousand to millions of points for each item, Real3D-AD is the
largest dataset for high-precision 3D industrial anomaly detection to date.
Real3D-AD surpasses existing 3D anomaly detection datasets available regarding
point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect
prototype. Additionally, we present a comprehensive benchmark for Real3D-AD,
revealing the absence of baseline methods for high-precision point cloud
anomaly detection. To address this, we propose Reg3D-AD, a registration-based
3D anomaly detection method incorporating a novel feature memory bank that
preserves local and global representations. Extensive experiments on the
Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility
and accessibility, we provide the Real3D-AD dataset, benchmark source code, and
Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD
Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning
According to the World Health Organization(WHO), it is estimated that
approximately 1.3 billion people live with some forms of vision impairment
globally, of whom 36 million are blind. Due to their disability, engaging these
minority into the society is a challenging problem. The recent rise of smart
mobile phones provides a new solution by enabling blind users' convenient
access to the information and service for understanding the world. Users with
vision impairment can adopt the screen reader embedded in the mobile operating
systems to read the content of each screen within the app, and use gestures to
interact with the phone. However, the prerequisite of using screen readers is
that developers have to add natural-language labels to the image-based
components when they are developing the app. Unfortunately, more than 77% apps
have issues of missing labels, according to our analysis of 10,408 Android
apps. Most of these issues are caused by developers' lack of awareness and
knowledge in considering the minority. And even if developers want to add the
labels to UI components, they may not come up with concise and clear
description as most of them are of no visual issues. To overcome these
challenges, we develop a deep-learning based model, called LabelDroid, to
automatically predict the labels of image-based buttons by learning from
large-scale commercial apps in Google Play. The experimental results show that
our model can make accurate predictions and the generated labels are of higher
quality than that from real Android developers.Comment: Accepted to 42nd International Conference on Software Engineerin
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