A Literature Review on the Application of Acoustic Emission to Machine Condition Monitoring

Acoustic emission (AE) is a common physical phenomenon, in which the strain energy is released in the form of elastic wave when a material is deformed or cracked during the stress process. The condition monitoring based on AE is a relatively new method that aims to use noise/vibration anomalies to detect machine failures. However, some challenges lie ahead of its application. This thesis aims to analyze the literature in the field of AE applications to machine condition monitoring. The principles of AE technology, relevant instruments, machine monitoring and AE signal analysis, and practical examples of AE monitoring applications will be presented. More specifically, challenges, solutions and future direction in solving signal noise and attenuation challenges will be discussed. Through the example of rotating machinery, the characteristics of AE will be explained in detail. This thesis lays the foundation for the actual use of AE to monitor and analyze the state of machinery and provides guideline for future data collection and analysis of AE signals

AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System

User behavior and feature interactions are crucial in deep learning-based recommender systems. There has been a diverse set of behavior modeling and interaction exploration methods in the literature. Nevertheless, the design of task-aware recommender systems still requires feature engineering and architecture engineering from domain experts. In this work, we introduce AMER, namely Automatic behavior Modeling and interaction Exploration in Recommender systems with Neural Architecture Search (NAS). The core contributions of AMER include the three-stage search space and the tailored three-step searching pipeline. In the first step, AMER searches for residual blocks that incorporate commonly used operations in the block-wise search space of stage 1 to model sequential patterns in user behavior. In the second step, it progressively investigates useful low-order and high-order feature interactions in the non-sequential interaction space of stage 2. Finally, an aggregation multi-layer perceptron (MLP) with shortcut connection is selected from flexible dimension settings of stage~3 to combine features extracted from the previous steps. For efficient and effective NAS, AMER employs the one-shot random search in all three steps. Further analysis reveals that AMER's search space could cover most of the representative behavior extraction and interaction investigation methods, which demonstrates the universality of our design. The extensive experimental results over various scenarios reveal that AMER could outperform competitive baselines with elaborate feature engineering and architecture engineering, indicating both effectiveness and robustness of the proposed method

AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort

Story visualization aims to generate a series of images that match the story described in texts, and it requires the generated images to satisfy high quality, alignment with the text description, and consistency in character identities. Given the complexity of story visualization, existing methods drastically simplify the problem by considering only a few specific characters and scenarios, or requiring the users to provide per-image control conditions such as sketches. However, these simplifications render these methods incompetent for real applications. To this end, we propose an automated story visualization system that can effectively generate diverse, high-quality, and consistent sets of story images, with minimal human interactions. Specifically, we utilize the comprehension and planning capabilities of large language models for layout planning, and then leverage large-scale text-to-image models to generate sophisticated story images based on the layout. We empirically find that sparse control conditions, such as bounding boxes, are suitable for layout planning, while dense control conditions, e.g., sketches and keypoints, are suitable for generating high-quality image content. To obtain the best of both worlds, we devise a dense condition generation module to transform simple bounding box layouts into sketch or keypoint control conditions for final image generation, which not only improves the image quality but also allows easy and intuitive user interactions. In addition, we propose a simple yet effective method to generate multi-view consistent character images, eliminating the reliance on human labor to collect or draw character images.Comment: 19 page

AutoAssign+: Automatic Shared Embedding Assignment in Streaming Recommendation

In the domain of streaming recommender systems, conventional methods for addressing new user IDs or item IDs typically involve assigning initial ID embeddings randomly. However, this practice results in two practical challenges: (i) Items or users with limited interactive data may yield suboptimal prediction performance. (ii) Embedding new IDs or low-frequency IDs necessitates consistently expanding the embedding table, leading to unnecessary memory consumption. In light of these concerns, we introduce a reinforcement learning-driven framework, namely AutoAssign+, that facilitates Automatic Shared Embedding Assignment Plus. To be specific, AutoAssign+ utilizes an Identity Agent as an actor network, which plays a dual role: (i) Representing low-frequency IDs field-wise with a small set of shared embeddings to enhance the embedding initialization, and (ii) Dynamically determining which ID features should be retained or eliminated in the embedding table. The policy of the agent is optimized with the guidance of a critic network. To evaluate the effectiveness of our approach, we perform extensive experiments on three commonly used benchmark datasets. Our experiment results demonstrate that AutoAssign+ is capable of significantly enhancing recommendation performance by mitigating the cold-start problem. Furthermore, our framework yields a reduction in memory usage of approximately 20-30%, verifying its practical effectiveness and efficiency for streaming recommender systems

Neural Dependencies Emerging from Learning Massive Categories

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call \textbf{neural dependency}. 2) Neural dependencies exist not only within a single model, but even between two independently learned models, regardless of their architectures. Towards a theoretical analysis of such phenomena, we demonstrate that identifying neural dependencies is equivalent to solving the Covariance Lasso (CovLasso) regression problem proposed in this paper. Through investigating the properties of the problem solution, we confirm that neural dependency is guaranteed by a redundant logit covariance matrix, which condition is easily met given massive categories, and that neural dependency is highly sparse, implying that one category correlates to only a few others. We further empirically show the potential of neural dependencies in understanding internal data correlations, generalizing models to unseen categories, and improving model robustness with a dependency-derived regularizer. Code for this work will be made publicly available

Linear Positional Isomer Sorting in Nonporous Adaptive Crystals of a Pillar[5]arene

Here we show a new adsorptive separation approach using nonporous adaptive crystals of a pillar[5]­arene. Desolvated perethylated pillar[5]­arene crystals (<b>EtP5</b>α) with a nonporous character selectively adsorb 1-pentene (<b>1-Pe</b>) over its positional isomer 2-pentene (<b>2-Pe</b>), leading to a structural change from <b>EtP5</b>α to <b>1-Pe</b> loaded structure (<b>1-Pe</b>@<b>EtP5</b>). The purity of <b>1-Pe</b> reaches 98.7% in just one cycle and <b>EtP5</b>α can be reused without losing separation performance

Mechanochemical synthesis of pillar[5]quinone derived multi-microporous organic polymers for radioactive organic iodide capture and storage.

The incorporation of supramolecular macrocycles into porous organic polymers may endow the material with enhanced uptake of specific guests through host-guest interactions. Here we report a solvent and catalyst-free mechanochemical synthesis of pillar[5]quinone (P5Q) derived multi-microporous organic polymers with hydrophenazine linkages (MHP-P5Q), which show a unique 3-step N2 adsorption isotherm. In comparison with analogous microporous hydrophenazine-linked organic polymers (MHPs) obtained using simple twofold benzoquinones, MHP-P5Q is demonstrated to have a superior performance in radioactive iodomethane (CH3I) capture and storage. Mechanistic studies show that the rigid pillar[5]arene cavity has additional binding sites though host-guest interactions as well as the halogen bond (-I⋯N = C-) and chemical adsorption in the multi-microporous MHP-P5Q mainly account for the rapid and high-capacity adsorption and long-term storage of CH3I

An Epistemic-Deontic-Axiologic (EDA) agent-based Energy Management System in office buildings

In the UK, buildings contribute about one third of the energy-related greenhouse gas emissions. Space heating and cooling systems are among the biggest energy consumers in buildings. This research aims to develop a novel Building Energy Management System (BEMS) to reduce the energy consumption of the heating, ventilation and air-conditioning (HVAC) system while fulfilling each occupant’ thermal comfort requirement. This paper presents a newly developed novel method, Epistemic-Deontic-Axiologic (EDA) Agent-based solution to support the Energy Management System meeting the dual targets of occupant thermal comfort and energy efficiency. The multi-agent solutions are applied to the BEMS. The problem decomposition method is used to define the architecture of the system. The Epistemic-Deontic-Axiologic (EDA) agent model is applied to develop the rational local and personal agents inside the system. These EDA-based agents select their optimal action plan by considering the occupants’ thermal sensations, their behavioural adaptations and the energy consumption of the HVAC system. The Newly-developed personal thermal sensation models and group-of-people-based thermal sensation models generated by support vector machine (SVM) based algorithms are applied to evaluate the occupants’ thermal sensations. These models are developed from the data collected in a real built environment. Simulation results prove that the newly-developed BEMS can help the HVAC system reduce the energy consumption by up to 10% while fulfilling the occupants’ thermal comfort requirements