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

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 $n$ is polynomially depending on the dimension $d$ of the samples, i.e., $n\asymp d^{\gamma}$ for some $\gamma >0$ ). 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 $\varepsilon_{n}^{2}$ and the metric entropy $\bar{\varepsilon}_{n}^{2}$ respectively. When the target function falls into the RKHS associated with a (general) inner product model defined on $\mathbb{S}^{d}$, we utilize the new tool to show that the minimax rate of the excess risk of kernel regression is $n^{-1/2}$ when $n\asymp d^{\gamma}$ for $\gamma =2, 4, 6, 8, \cdots$. We then further determine the optimal rate of the excess risk of kernel regression for all the $\gamma>0$ and find that the curve of optimal rate varying along $\gamma$ 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