Maintenance policy for two-stage deteriorating mode system based on cumulative damage model

For the system degradation process undergoing a sudden change, optimal maintenance policies were developed using the cumulative damage model and two-stage degradation modeling. Single shock damage value and the number of shock times are assumed to be normal distribution and homogeneous Poisson process, respectively. On this basis, average long-run cost rate of a renewal cycle was modeled with considering the probabilities of corrective, preventive and continuous monitoring, respectively. In order to develop an optimal policy, four types of maintenance policies (i.e., global, time-depended, adaptive and simplified adaptive policies) were analyzed with different alarm thresholds and inter-inspection time. Influence analysis of different parameters for maintenance policy was given, where different maintenance policies were compared in terms of average long-run cost rate. In addition, the impacts of degradation model parameters (i.e., change-point distribution, shock strength, shock frequency) on the average long-run cost rate were analyzed. Finally, maintenance policy for gearbox degradation experiment was analyzed in case study

Mapping the HealthPathways literature: a scoping review protocol

Objective: This scoping review will identify what literature exists on HealthPathways and make suggestions for the direction of future HealthPathways research. Background: HealthPathways is a free to access, password protected online tool containing practical, easy to use, localised clinical and referral information that is primarily aimed at GPs. HealthPathways originated in Canterbury, New Zealand in 2008. Since this time the program has spread and is being used in 50 health systems across New Zealand, Australia, and the United Kingdom (Streamliners, 2022a). Despite such large spread of the program there has been relatively little literature published on the utility, usefulness and cost-effectiveness of HealthPathways. This scoping review aims to identify and describe all current HealthPathways literature and make recommendations for the direction of future HealthPathways research. Methods: The Joanna Briggs Institute (JBI) methodology will be used to develop the scoping review. Databases included in the search include MEDLINE (PubMEd), Embase, CINAHL, Web of Science, Google Scholar, Emerald and Cochrane. The inclusion criteria are studies and grey literature on HealthPathways that are published in English, with no time limit. Grey literature will be identified through searching relevant credible organisations and websites. All results will be entered into Covidence to be assessed by two reviewers against a set tool. The PRISMA extension for scoping reviews will be used for reporting. Ethics approval is not required as only published information will be used. The research will be disseminated through publication in an open access peer reviewed journal. Conclusions: This protocol is published to make the process for the review transparent and replicable. The scoping review will highlight the extent of evidence that exists on HealthPathways and may provide direction for decision making and future research

Boosting Point Clouds Rendering via Radiance Mapping

Recent years we have witnessed rapid development in NeRF-based image rendering due to its high quality. However, point clouds rendering is somehow less explored. Compared to NeRF-based rendering which suffers from dense spatial sampling, point clouds rendering is naturally less computation intensive, which enables its deployment in mobile computing device. In this work, we focus on boosting the image quality of point clouds rendering with a compact model design. We first analyze the adaption of the volume rendering formulation on point clouds. Based on the analysis, we simplify the NeRF representation to a spatial mapping function which only requires single evaluation per pixel. Further, motivated by ray marching, we rectify the the noisy raw point clouds to the estimated intersection between rays and surfaces as queried coordinates, which could avoid spatial frequency collapse and neighbor point disturbance. Composed of rasterization, spatial mapping and the refinement stages, our method achieves the state-of-the-art performance on point clouds rendering, outperforming prior works by notable margins, with a smaller model size. We obtain a PSNR of 31.74 on NeRF-Synthetic, 25.88 on ScanNet and 30.81 on DTU. Code and data would be released soon

General Debiasing for Multimodal Sentiment Analysis

Existing work on Multimodal Sentiment Analysis (MSA) utilizes multimodal information for prediction yet unavoidably suffers from fitting the spurious correlations between multimodal features and sentiment labels. For example, if most videos with a blue background have positive labels in a dataset, the model will rely on such correlations for prediction, while ``blue background'' is not a sentiment-related feature. To address this problem, we define a general debiasing MSA task, which aims to enhance the Out-Of-Distribution (OOD) generalization ability of MSA models by reducing their reliance on spurious correlations. To this end, we propose a general debiasing framework based on Inverse Probability Weighting (IPW), which adaptively assigns small weights to the samples with larger bias i.e., the severer spurious correlations). The key to this debiasing framework is to estimate the bias of each sample, which is achieved by two steps: 1) disentangling the robust features and biased features in each modality, and 2) utilizing the biased features to estimate the bias. Finally, we employ IPW to reduce the effects of large-biased samples, facilitating robust feature learning for sentiment prediction. To examine the model's generalization ability, we keep the original testing sets on two benchmarks and additionally construct multiple unimodal and multimodal OOD testing sets. The empirical results demonstrate the superior generalization ability of our proposed framework. We have released the code and data to facilitate the reproduction

A Regular Pattern of Timestamps Between Machines with Built-in System Time

This paper studied the effect of 15.6 ms time resolution where the collected timestamps are in a form of parallel dotted lines, instead of one straight line like in classical case. The dotted lines made the clock skew measurement of two devices to become incorrect as the measurement which normally follow the cluster of offsets but now follow the parallel dotted lines. Dotted lines pattern is required in order to understand how to correct the clock skew measurement on data containing dotted lines. To model the dotted lines pattern is through Dotted lines Grouping Method, a tools to find the characteristics of the dotted lines. The dotted lines grouping method was then tested data obtained from wired and wireless communication of two similar devices. The dotted line grouping method results equal maximum number of dot of 10 for both data, which indicated the robustness of the dotted lines grouping method

Adversarial Domain Adaptation with Domain Mixup

Recent works on domain adaptation reveal the effectiveness of adversarial learning on filling the discrepancy between source and target domains. However, two common limitations exist in current adversarial-learning-based methods. First, samples from two domains alone are not sufficient to ensure domain-invariance at most part of latent space. Second, the domain discriminator involved in these methods can only judge real or fake with the guidance of hard label, while it is more reasonable to use soft scores to evaluate the generated images or features, i.e., to fully utilize the inter-domain information. In this paper, we present adversarial domain adaptation with domain mixup (DM-ADA), which guarantees domain-invariance in a more continuous latent space and guides the domain discriminator in judging samples' difference relative to source and target domains. Domain mixup is jointly conducted on pixel and feature level to improve the robustness of models. Extensive experiments prove that the proposed approach can achieve superior performance on tasks with various degrees of domain shift and data complexity.Comment: Accepted as oral presentation at 34th AAAI Conference on Artificial Intelligence, 202

Air Quality Monitoring and Data Acquisition for Livestock and Poultry Environment Studies

The development of analytical instruments and computer technologies in recent decades has facilitated significant changes in the methodologies used in scientific studies of agricultural air quality. A variety of instruments and sensors have been used for long-term and continuous measurements at commercial animal facilities and laboratories for determining baseline pollutant emissions and testing mitigation technologies. New measurement strategies were developed for real-time measurement and multi-location sampling. Optimization of this technology change necessitates an up-to-date system to acquire high-frequency data, control instruments and sampling locations, and monitor system operation. While various air quality research projects involve similar objectives and instrumentation to meet those objectives, they are usually conducted with monitoring plans that differ among sites and among projects. Special data acquisition and control (DAC) hardware and software have to be adapted for each monitoring plan. This paper summarizes various measurement and control devices used for comprehensive air quality studies of livestock and poultry environments. The paper further presents methods for real-time data transformation and processing. It introduces an air quality DAC system, which provided novel, flexible, and user-friendly features. The methodology and technology used in the new DAC system reduces system development and operational cost, increase reliability and work efficiency, and enhances data quality