Distributed Anomaly Detection using Autoencoder Neural Networks in WSN for IoT

Wireless sensor networks (WSN) are fundamental to the Internet of Things (IoT) by bridging the gap between the physical and the cyber worlds. Anomaly detection is a critical task in this context as it is responsible for identifying various events of interests such as equipment faults and undiscovered phenomena. However, this task is challenging because of the elusive nature of anomalies and the volatility of the ambient environments. In a resource-scarce setting like WSN, this challenge is further elevated and weakens the suitability of many existing solutions. In this paper, for the first time, we introduce autoencoder neural networks into WSN to solve the anomaly detection problem. We design a two-part algorithm that resides on sensors and the IoT cloud respectively, such that (i) anomalies can be detected at sensors in a fully distributed manner without the need for communicating with any other sensors or the cloud, and (ii) the relatively more computation-intensive learning task can be handled by the cloud with a much lower (and configurable) frequency. In addition to the minimal communication overhead, the computational load on sensors is also very low (of polynomial complexity) and readily affordable by most COTS sensors. Using a real WSN indoor testbed and sensor data collected over 4 consecutive months, we demonstrate via experiments that our proposed autoencoder-based anomaly detection mechanism achieves high detection accuracy and low false alarm rate. It is also able to adapt to unforeseeable and new changes in a non-stationary environment, thanks to the unsupervised learning feature of our chosen autoencoder neural networks.Comment: 6 pages, 7 figures, IEEE ICC 201

Three-dimensional modelling on the hydrodynamics of a circulating fluidised bed

The rapid depletion of oil and the environmentalimpact of combustion has motivated the search for cleancombustion technologies. Fluidised bed combustion (FBC)technology works by suspending a fuel over a fast air inletwhilst sustaining the required temperatures. Using biomassor a mixture of coal/biomass as the fuel, FBC provides alow-carbon combustion technology whilst operating at lowtemperatures. Understanding the hydrodynamic processes influidised beds is essential as the flow behaviours causing heatdistributions and mixing determine the combustion processes.The inlet velocities and different particle sizes influence theflow behaviour significantly, particularly on the transitionfrom bubbling to fast fluidising regimes. Computationalmodelling has shown great advancement in its predictive capabilityand reliability over recent years. Whilst 3D modellingis preferred over 2D modelling, the majority of studies use2D models for multiphase models due to computational costconsideration. In this paper, two-fluid modelling (TFM) isused to model a 3D circulating fluidised bed (CFB) initiallyfocussing on fluid catalytic cracker (FCC) particles. Thetransition from bubbling to fast fluidisation over a rangeof velocities is explored, whilst the effects on the bubblediameter, particle distributions and bed expansion for differentparticle properties including particle sizes are compared. Dragmodels are also compared to study the effects of particleclustering at the meso-scale

ESSAYS ON SKILLS AND RACIAL GAPS IN THE U.S. LABOR MARKET

In this dissertation I establish some of the first evidence on the early career labor market experiences of young American men from the Millennial cohort. I also conduct a cross-cohort comparison of the early career outcomes of Millennials compared to their predecessors from the Baby Boomer cohort. The empirical analysis in this dissertation is facilitated by the 1997 and 1979 samples of the National Longitudinal Survey of Youth (NLSY). First, I document the racial gaps in early career labor market trajectories of a cohort of early Millennial men (NLSY–97, born 1980–1984), and explore the driving forces behind them. Tracing the experiences of Black and white young men over their first eight years after school completion, I show that racial gaps in various labor market outcomes opened up immediately post-schooling, and largely persisted over the subsequent years. In particular, I find that measured Black-white disparities in accumulated education and skills, especially cognitive skills, play the central role in explaining the observed racial gaps in employment and earnings. Second, I compare how the racial labor market gaps have changed between the Baby Boomers (NLSY–79, born 1957–1964) add these Millennials. Both Black and white men in the older cohort experienced upward-sloping trajectories in employment and earnings in the first four to five years post-schooling. In the younger cohort, the labor market trajectories, especially for employment, were comparatively flatter both for Black men and for white men. Relative to the older cohort, a larger share of the racial employment and earnings gaps in the younger cohort cannot be explained by measured racial differences in observable premarket characteristics. Yet education and skills remain the key explanatory factor among observable characteristics. Third, in co-authored work, we examine how the wage returns to cognitive skills have evolved across cohorts of white men in the U.S. labor market. We show that the distribution of measured cognitive skills has diverged between the NLSY–79 and the NLSY–97. This divergence has a meaningful impact on estimated returns to cognitive skills. We explore why this divergence has occurred, considering both economic and measurement explanations, and we conclude that the conventional wisdom of a declining return to cognitive skills may well be incorrect

BIM: Block-Wise Self-Supervised Learning with Masked Image Modeling

Like masked language modeling (MLM) in natural language processing, masked image modeling (MIM) aims to extract valuable insights from image patches to enhance the feature extraction capabilities of the underlying deep neural network (DNN). Contrasted with other training paradigms like supervised learning and unsupervised contrastive learning, masked image modeling (MIM) pretraining typically demands significant computational resources in order to manage large training data batches (e.g., 4096). The significant memory and computation requirements pose a considerable challenge to its broad adoption. To mitigate this, we introduce a novel learning framework, termed~\textit{Block-Wise Masked Image Modeling} (BIM). This framework involves decomposing the MIM tasks into several sub-tasks with independent computation patterns, resulting in block-wise back-propagation operations instead of the traditional end-to-end approach. Our proposed BIM maintains superior performance compared to conventional MIM while greatly reducing peak memory consumption. Moreover, BIM naturally enables the concurrent training of numerous DNN backbones of varying depths. This leads to the creation of multiple trained DNN backbones, each tailored to different hardware platforms with distinct computing capabilities. This approach significantly reduces computational costs in comparison with training each DNN backbone individually. Our framework offers a promising solution for resource constrained training of MIM

In a free healthcare system, why do men not consult for lower urinary tract symptoms (LUTS)?

<p>Abstract</p> <p>Background</p> <p>The prevalence of lower urinary tract symptoms (LUTS) varies among different populations but the rate of seeking medical advice is consistently low. Little is known about the reasons for this low rate. In the city of Macau, China, primary healthcare is free and easily accessible to all citizens. We aim to study the patients' rate of consulting for LUTS and their reasons for not consulting under a free healthcare system.</p> <p>Method</p> <p>A convenience sample of 549 male patients aged 40-85 years in a government health centre filled in the International Prostate Symptoms Scale (IPSS) questionnaire. They were also asked if they had consulted doctors for LUTS, and if not, why not.</p> <p>Result</p> <p>Of the whole sample, 64 men (11.7%) had ever consulted doctors for LUTS. Of 145 with moderate to severe LUTS, 35 (24.1%) consulted. Of 73 who were dissatisfied with their quality of life, 22 (30.1%) consulted. Regarding the symptoms as normal or not problematic was the main reason for not consulting. Advancing age and duration of symptoms were the significant factors for consulting.</p> <p>Conclusion</p> <p>Primary care doctors could help many of LUTS patients by sensitively initiating the discussion when these patients consult for other problems.</p

Optimal Control Method of Parabolic Partial Differential Equations and Its Application to Heat Transfer Model in Continuous Cast Secondary Cooling Zone

Our work is devoted to a class of optimal control problems of parabolic partial differential equations. Because of the partial differential equations constraints, it is rather difficult to solve the optimization problem. The gradient of the cost function can be found by the adjoint problem approach. Based on the adjoint problem approach, the gradient of cost function is proved to be Lipschitz continuous. An improved conjugate method is applied to solve this optimization problem and this algorithm is proved to be convergent. This method is applied to set-point values in continuous cast secondary cooling zone. Based on the real data in a plant, the simulation experiments show that the method can ensure the steel billet quality. From these experiment results, it is concluded that the improved conjugate gradient algorithm is convergent and the method is effective in optimal control problem of partial differential equations