Wind Turbine Blade Bearing Fault Detection with Bayesian and Adaptive Kalman Augmented Lagrangian Algorithm

As a critically supporting and rotational component for wind turbines, blade bearings need special health monitoring for safe operation in actual industrial conditions. One of the main difficulties of the wind turbine blade bearing condition monitoring is noisy signals generated under fluctuating slow speed with heavy loads. This is because blade bearing rotation speed is influenced by blade flipping and external disturbances, and this influence is time-varying. This paper proposes a new method, Bayesian and Adapted Kalman Augmented Lagrangian (BAKAL), to filter the signal under this time-varying condition. The new method uses a two-step search (coarse and fine search) to deal with the filtering process based on Bayesian Augmented Lagrangian (BAL) framework. In addition, both linear and nonlinear effects and their sparsity are considered for model construction. Finally, the smearing problem in the frequency spectrum is dealt with through signal resample in the order domain for superior performance of fault diagnosis. The proposed BAKAL algorithm is strictly validated in several experiments under approximately fixed speed and variable speed within the condition of heavy loadings. The experiments use an industrial and rotational wind turbine blade bearing with natural defects, which has been served in an actual wind power plant for over 15 years. The experimental results demonstrate the effectiveness of the proposed method.<br/

PATE-TripleGAN: Privacy-Preserving Image Synthesis with Gaussian Differential Privacy

Conditional Generative Adversarial Networks (CGANs) exhibit significant potential in supervised learning model training by virtue of their ability to generate realistic labeled images. However, numerous studies have indicated the privacy leakage risk in CGANs models. The solution DPCGAN, incorporating the differential privacy framework, faces challenges such as heavy reliance on labeled data for model training and potential disruptions to original gradient information due to excessive gradient clipping, making it difficult to ensure model accuracy. To address these challenges, we present a privacy-preserving training framework called PATE-TripleGAN. This framework incorporates a classifier to pre-classify unlabeled data, establishing a three-party min-max game to reduce dependence on labeled data. Furthermore, we present a hybrid gradient desensitization algorithm based on the Private Aggregation of Teacher Ensembles (PATE) framework and Differential Private Stochastic Gradient Descent (DPSGD) method. This algorithm allows the model to retain gradient information more effectively while ensuring privacy protection, thereby enhancing the model's utility. Privacy analysis and extensive experiments affirm that the PATE-TripleGAN model can generate a higher quality labeled image dataset while ensuring the privacy of the training data

Accelerating Quadratic Transform and WMMSE

Fractional programming (FP) arises in various communications and signal processing problems because several key quantities in the field are fractionally structured, e.g., the Cram\'{e}r-Rao bound, the Fisher information, and the signal-to-interference-plus-noise ratio (SINR). A recently proposed method called the quadratic transform has been applied to the FP problems extensively. The main contributions of the present paper are two-fold. First, we investigate how fast the quadratic transform converges. To the best of our knowledge, this is the first work that analyzes the convergence rate for the quadratic transform as well as its special case the weighted minimum mean square error (WMMSE) algorithm. Second, we accelerate the existing quadratic transform via a novel use of Nesterov's extrapolation scheme [1]. Specifically, by generalizing the minorization-maximization (MM) approach in [2], we establish a nontrivial connection between the quadratic transform and the gradient projection, thereby further incorporating the gradient extrapolation into the quadratic transform to make it converge more rapidly. Moreover, the paper showcases the practical use of the accelerated quadratic transform with two frontier wireless applications: integrated sensing and communications (ISAC) and massive multiple-input multiple-output (MIMO).Comment: 15 page

Semi-Markov jump linear systems with bi-boundary sojourn time: Anti-modal-asynchrony control

This paper investigates the problem of control synthesis for a class of discrete-time semi-Markov jump linear systems, in which the sojourn time of each mode is bi-boundary (with upper and lower bounds). The system is subject to modal asynchrony, which means that the switchings of the mode-dependent controller to be designed lag behind the ones of the controlled plant, and the lag is mode-dependent. In contrast with the traditional mode-independent lag commonly assumed in the existing studies, not only is the modal lag more practical and general, but also it yields less conservatism of the controller design. By virtue of the semi-Markov kernel approach, the conditions on the existence of the anticipated stabilizing controllers capable of overcoming the modal asynchrony are derived. Illustrative examples including a class of vertical take-off and landing (VTOL) helicopter models are presented to demonstrate the necessity and the validity of the designed anti-modal-asynchrony controllers

The Long-Baseline Neutrino Experiment: Exploring Fundamental Symmetries of the Universe

The preponderance of matter over antimatter in the early Universe, the dynamics of the supernova bursts that produced the heavy elements necessary for life and whether protons eventually decay --- these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our Universe, its current state and its eventual fate. The Long-Baseline Neutrino Experiment (LBNE) represents an extensively developed plan for a world-class experiment dedicated to addressing these questions. LBNE is conceived around three central components: (1) a new, high-intensity neutrino source generated from a megawatt-class proton accelerator at Fermi National Accelerator Laboratory, (2) a near neutrino detector just downstream of the source, and (3) a massive liquid argon time-projection chamber deployed as a far detector deep underground at the Sanford Underground Research Facility. This facility, located at the site of the former Homestake Mine in Lead, South Dakota, is approximately 1,300 km from the neutrino source at Fermilab -- a distance (baseline) that delivers optimal sensitivity to neutrino charge-parity symmetry violation and mass ordering effects. This ambitious yet cost-effective design incorporates scalability and flexibility and can accommodate a variety of upgrades and contributions. With its exceptional combination of experimental configuration, technical capabilities, and potential for transformative discoveries, LBNE promises to be a vital facility for the field of particle physics worldwide, providing physicists from around the globe with opportunities to collaborate in a twenty to thirty year program of exciting science. In this document we provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess.Comment: Major update of previous version. This is the reference document for LBNE science program and current status. Chapters 1, 3, and 9 provide a comprehensive overview of LBNE's scientific objectives, its place in the landscape of neutrino physics worldwide, the technologies it will incorporate and the capabilities it will possess. 288 pages, 116 figure

Circulating soluble suppression of tumorigenicity-2 and the recurrence of atrial fibrillation after catheter ablation: A meta-analysis

Soluble suppression of tumorigenicity-2 (sST-2), a marker of myocardial fibrosis and remodeling, has been related to the development of atrial fibrillation (AF). The aim of this meta-analysis was to evaluate the relationship between baseline serum sST-2 levels and the risk of AF recurrence after ablation. Relevant observational studies were retrieved from PubMed, Web of Science, Embase, Wanfang and China National Knowledge Infrastructure (CNKI). A random-effects model was used to combine the data, accounting for between-study heterogeneity. Fourteen prospective cohorts were included. Pooled results showed higher sST-2 levels before ablation in patients with AF recurrence compared to those without AF recurrence (standardized mean difference = 1.15, 95% confidence interval [CI] = 0.67 to 1.63, P < 0.001; I2 = 92%). Meta-regression analysis suggested that the proportion of patients with paroxysmal AF (PaAF) was positively related to the difference in serum sST-2 levels between patients with and without AF recurrence (coefficient = 0.033, P < 0.001). Subgroup analysis showed a more remarkable difference in serum sST-2 levels between patients with and without AF recurrence in studies where PaAF was ≥ 60% compared to those where it was < 60% (P = 0.007). Further analyses showed that high sST-2 levels before ablation were associated with an increased risk of AF recurrence (odds ratio [OR] per 1 ng/mL increment of sST-2 =1.05, OR for high versus low sST-2 = 1.73, both P values < 0.05). In conclusion, high sST-2 baseline levels may be associated with an increased risk of AF recurrence after catheter ablation

Perspective Dynamic covalent chemistry toward wearable electronics

Dynamic covalent chemistry (DCvC) has become an important tool in developing smart materials in the past few decades. By incorporating reversible covalent bonds into crosslinked polymer networks, those features that are highly desired in wearable electronics, such as stretchability, malleability, self-healing capability, and closed-loop recyclability, could be achieved. In this perspective, we briefly summarize how DCvC influences the development of dynamic polymer networks and discuss their applications in wearable electronics. The advantages and challenges of wearable electronics with dynamic covalent features are also discussed. In addition, forward-thinking outlooks for future directions of utilizing DCvC in the development of wearable electronics are provided. &nbsp;</p

Kuaipedia: a Large-scale Multi-modal Short-video Encyclopedia

Online encyclopedias, such as Wikipedia, have been well-developed and researched in the last two decades. One can find any attributes or other information of a wiki item on a wiki page edited by a community of volunteers. However, the traditional text, images and tables can hardly express some aspects of an wiki item. For example, when we talk about ``Shiba Inu'', one may care more about ``How to feed it'' or ``How to train it not to protect its food''. Currently, short-video platforms have become a hallmark in the online world. Whether you're on TikTok, Instagram, Kuaishou, or YouTube Shorts, short-video apps have changed how we consume and create content today. Except for producing short videos for entertainment, we can find more and more authors sharing insightful knowledge widely across all walks of life. These short videos, which we call knowledge videos, can easily express any aspects (e.g. hair or how-to-feed) consumers want to know about an item (e.g. Shiba Inu), and they can be systematically analyzed and organized like an online encyclopedia. In this paper, we propose Kuaipedia, a large-scale multi-modal encyclopedia consisting of items, aspects, and short videos lined to them, which was extracted from billions of videos of Kuaishou (Kwai), a well-known short-video platform in China. We first collected items from multiple sources and mined user-centered aspects from millions of users' queries to build an item-aspect tree. Then we propose a new task called ``multi-modal item-aspect linking'' as an expansion of ``entity linking'' to link short videos into item-aspect pairs and build the whole short-video encyclopedia. Intrinsic evaluations show that our encyclopedia is of large scale and highly accurate. We also conduct sufficient extrinsic experiments to show how Kuaipedia can help fundamental applications such as entity typing and entity linking

Magnetic Assembly and Functionalization of One-Dimensional Nanominerals in Optical Field

Magnetic particles can be oriented along the magnetic field direction to achieve orderly arrangement under the magnetic field. Optical functional materials such as photonic crystal and liquid crystal can be obtained according to magnetic induced ordered nanostructure assembly. One-dimensional natural clay minerals with unique structure, composition and properties can be used as structural base to prepare anisotropic magnetic nanoparticles by decorated with magnetic particles, achieving unique optical functional properties. In this chapter, one-dimensional clay minerals@Fe3O4 nanocomposites were prepared by co-precipitation. The resulting one-dimensional clay minerals@Fe3O4 nanocomposites are superparamagnetic. They can be oriented along the direction of the magnetic field and produce an instantaneously reversible response. These magnetic mineral materials can be dispersed in a dilute acid solution to form stable colloid solutions. These stable colloid solutions produce a similar magnetically controlled liquid crystal with Bragg diffraction under an external magnetic field. Their optical properties are affected by magnetic field intensity, magnetic field direction and solid content. The results show that the functionalization of one-dimensional clay minerals has potential applications in display devices, photonic switches and other fields

Handover algorithm for space-air-ground integrated network based on location prediction model

To address the issues of frequent handovers and network load imbalance caused by dynamic changes in the network environment and enhanced mobility of user terminals in the 6G space-air-ground integrated network (SAGIN), a handover algorithm for SAGIN based on a terminal location prediction model was proposed. The algorithm constructed a long short-term memory (LSTM) network terminal location prediction model optimized based on the sparrow search strategy, improving the accuracy of terminal location prediction and resolving the issue of unreasonable handover timing. Based on this model, the SAGIN selection problem was modeled as a Markov decision process. A network handover algorithm utility function characterized by quality of service (QoS) requirements, handover cost, and network load balancing was designed. A distributional deep Q-network (D-DQN) was employed to select the network nodes that could maximize long-term goals for execution handover. Compared with network handover algorithms based on Q-Learning, double deep Q-network (DDQN), and dueling double deep Q-network (D3QN), the proposed algorithm performs better in terms of reducing handover delay and frequency, as well as enhancing network throughput, thereby validating the effectiveness of the proposed algorithm