81 research outputs found
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
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]Âarene. Desolvated perethylated pillar[5]Âarene
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
Recommended from our members
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
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
- …