275 research outputs found
Real time defect detection in welds by ultrasonic means
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A computer controlled weld quality assurance system has been developed to detect weld defects ultrasonically whilst welding is in progress. This system, including a flash analogue to digital converter and built-in memories to store sampled data, a peak characters extractor and a welding process controller, enabled welding processes to be controlled automatically and welding defects to be detected concurrently with welding. In this way, the weld quality could be satisfactorily assured if no defect was detected and the welding cost was minimised either through avoiding similar defects to occur or by stopping the welding process if repair was necessary. This work demonstrated that the high temperature field around the weld pool was the major source of difficulties and unreliabilities in defect detection during welding and, had to be taken into account in welding control by ultrasonic means. The high temperatures not only influence ultrasonic characteristic parameters which are the defect judgement and assessment criterion, but also introduce noise into signals. The signal averaging technique and statistical analysis based on B-scan data have proved their feasibility to increase 'signal to noise ratio' effectively and to judge or assess weld defects. The hardware and the software for the system is explained in this work. By using this system, real-time 'A-scan' signals on screen display, and, A-scan, B-scan or three dimensional results can be printed on paper, or stored on disks, and, as a result, weld quality could be fully computerized.Sino-British Friendship Scholarship Schem
Learning Multi-Object Positional Relationships via Emergent Communication
The study of emergent communication has been dedicated to interactive
artificial intelligence. While existing work focuses on communication about
single objects or complex image scenes, we argue that communicating
relationships between multiple objects is important in more realistic tasks,
but understudied. In this paper, we try to fill this gap and focus on emergent
communication about positional relationships between two objects. We train
agents in the referential game where observations contain two objects, and find
that generalization is the major problem when the positional relationship is
involved. The key factor affecting the generalization ability of the emergent
language is the input variation between Speaker and Listener, which is realized
by a random image generator in our work. Further, we find that the learned
language can generalize well in a new multi-step MDP task where the positional
relationship describes the goal, and performs better than raw-pixel images as
well as pre-trained image features, verifying the strong generalization ability
of discrete sequences. We also show that language transfer from the referential
game performs better in the new task than learning language directly in this
task, implying the potential benefits of pre-training in referential games. All
in all, our experiments demonstrate the viability and merit of having agents
learn to communicate positional relationships between multiple objects through
emergent communication.Comment: 15 page
An Integrated Enhancement Solution for 24-hour Colorful Imaging
The current industry practice for 24-hour outdoor imaging is to use a silicon
camera supplemented with near-infrared (NIR) illumination. This will result in
color images with poor contrast at daytime and absence of chrominance at
nighttime. For this dilemma, all existing solutions try to capture RGB and NIR
images separately. However, they need additional hardware support and suffer
from various drawbacks, including short service life, high price, specific
usage scenario, etc. In this paper, we propose a novel and integrated
enhancement solution that produces clear color images, whether at abundant
sunlight daytime or extremely low-light nighttime. Our key idea is to separate
the VIS and NIR information from mixed signals, and enhance the VIS signal
adaptively with the NIR signal as assistance. To this end, we build an optical
system to collect a new VIS-NIR-MIX dataset and present a physically meaningful
image processing algorithm based on CNN. Extensive experiments show outstanding
results, which demonstrate the effectiveness of our solution.Comment: AAAI 2020 (Oral
Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation
Annotation scarcity has become a major obstacle for training powerful
deep-learning models for medical image segmentation, restricting their
deployment in clinical scenarios. To address it, semi-supervised learning by
exploiting abundant unlabeled data is highly desirable to boost the model
training. However, most existing works still focus on limited medical tasks and
underestimate the potential of learning across diverse tasks and multiple
datasets. Therefore, in this paper, we introduce a \textbf{Ver}satile
\textbf{Semi}-supervised framework (VerSemi) to point out a new perspective
that integrates various tasks into a unified model with a broad label space, to
exploit more unlabeled data for semi-supervised medical image segmentation.
Specifically, we introduce a dynamic task-prompted design to segment various
targets from different datasets. Next, this unified model is used to identify
the foreground regions from all labeled data, to capture cross-dataset
semantics. Particularly, we create a synthetic task with a cutmix strategy to
augment foreground targets within the expanded label space. To effectively
utilize unlabeled data, we introduce a consistency constraint. This involves
aligning aggregated predictions from various tasks with those from the
synthetic task, further guiding the model in accurately segmenting foreground
regions during training. We evaluated our VerSemi model on four public
benchmarking datasets. Extensive experiments demonstrated that VerSemi can
consistently outperform the second-best method by a large margin (e.g., an
average 2.69\% Dice gain on four datasets), setting new SOTA performance for
semi-supervised medical image segmentation. The code will be released
Scientific Knowledge Communication in Online Q&A Communities: Linguistic Devices as a Tool to Increase the Popularity and Perceived Professionalism of Knowledge Contribution
With the popularity of question-and-answer (Q&A) communities, widespread dissemination of scientific knowledge has become more viable than ever before. However, those contributing high-quality professional scientific knowledge are confronted with the challenge of making their contributions popular, since non expert readers may not recognize the importance of their contributions given the massive amount of information available online. In this study, we show that non expert readers are capable of evaluating the professionalism of content contributed in such communities as well as experts. However, we discovered that a salient discrepancy exists between the content non experts favor and the content they perceive as professional. In line with studies that have suggested that writing techniques play an important role in how expert content is received by lay persons, we investigated how the use of linguistic devices affects both the perceived professionalism and the popularity of contributions in Q&A communities. Based on both secondary data and a scenario-based survey, we identified specific linguistic devices that can increase content popularity without reducing perceived professionalism. Additionally, we revealed linguistic devices that increase popularity at the expense of perceived professionalism in this context. Finally, we conducted a laboratory experiment to more firmly establish the causal effects of the linguistic device use. The triangulated findings have important implications for both research and practice on communicating scientific knowledge in Q&A communitie
Striking a Balance: Harnessing Both the Business and Informational Value of Online Reviews through Resource-matching
LLaMA Rider: Spurring Large Language Models to Explore the Open World
Recently, various studies have leveraged Large Language Models (LLMs) to help
decision-making and planning in environments, and try to align the LLMs'
knowledge with the world conditions. Nonetheless, the capacity of LLMs to
continuously acquire environmental knowledge and adapt in an open world remains
uncertain. In this paper, we propose an approach to spur LLMs to explore the
open world, gather experiences, and learn to improve their task-solving
capabilities. In this approach, a multi-round feedback-revision mechanism is
utilized to encourage LLMs to actively select appropriate revision actions
guided by feedback information from the environment. This facilitates
exploration and enhances the model's performance. Besides, we integrate
sub-task relabeling to assist LLMs in maintaining consistency in sub-task
planning and help the model learn the combinatorial nature between tasks,
enabling it to complete a wider range of tasks through training based on the
acquired exploration experiences. By evaluation in Minecraft, an open-ended
sandbox world, we demonstrate that our approach LLaMA-Rider enhances the
efficiency of the LLM in exploring the environment, and effectively improves
the LLM's ability to accomplish more tasks through fine-tuning with merely 1.3k
instances of collected data, showing minimal training costs compared to the
baseline using reinforcement learning.Comment: 18 page
Optimal Rate of Kernel Regression in Large Dimensions
We perform a study on kernel regression for large-dimensional data (where the
sample size is polynomially depending on the dimension of the samples,
i.e., for some ). We first build a general
tool to characterize the upper bound and the minimax lower bound of kernel
regression for large dimensional data through the Mendelson complexity
and the metric entropy
respectively. When the target function falls into the RKHS associated with a
(general) inner product model defined on , we utilize the new
tool to show that the minimax rate of the excess risk of kernel regression is
when for . We then
further determine the optimal rate of the excess risk of kernel regression for
all the and find that the curve of optimal rate varying along
exhibits several new phenomena including the {\it multiple descent
behavior} and the {\it periodic plateau behavior}. As an application, For the
neural tangent kernel (NTK), we also provide a similar explicit description of
the curve of optimal rate. As a direct corollary, we know these claims hold for
wide neural networks as well
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