To improve the bit error rate (BER) of underwater wireless laser communication and increase communication speed, a duplex underwater wireless laser communication system is designed. The system uses ...both blue and green lasers to transmit signals simultaneously, and adopts an improved modulation and demodulation algorithm to achieve separate modulation of the two sets of signals. A duplex underwater wireless laser communication system builds based on 440 and 550 nm lasers. On the basis of analyzing the absorption and scattering characteristics of seawater in laser communication, the influence of turbulence on communication efficiency is discussed. Improved the traditional duplex modulation algorithm and conducted communication testing at a depth of 2.0 m underwater. The experimental results show that when the communication distance increases from 5 to 50 m, the BER of the unoptimized duplex mode increases from 2.34 × 10
−7
to 3.5 × 10
−5
. After adopting the optimization algorithm, the BER increases from 2.81 × 10
−7
to 1.18 × 10
−6
, and the signal attenuation has been significantly suppressed. The duplex communication structure and algorithm can effectively reduce the impact of communication distance on bit BER.
Objective To evaluate the effectiveness of machine learning (ML) models in predicting 5-year type 2 diabetes mellitus (T2DM) risk within the Chinese population by retrospectively analyzing annual ...health checkup records. Methods We included 46,247 patients (32,372 and 13,875 in training and validation sets, respectively) from a national health checkup center database. Univariate and multivariate Cox analyses were performed to identify factors influencing T2DM risk. Extreme Gradient Boosting (XGBoost), support vector machine (SVM), logistic regression (LR), and random forest (RF) models were trained to predict 5-year T2DM risk. Model performances were analyzed using receiver operating characteristic (ROC) curves for discrimination and calibration plots for prediction accuracy. Results Key variables included fasting plasma glucose, age, and sedentary time. The LR model showed good accuracy with respective areas under the ROC (AUCs) of 0.914 and 0.913 in training and validation sets; the RF model exhibited favorable AUCs of 0.998 and 0.838. In calibration analysis, the LR model displayed good fit for low-risk patients; the RF model exhibited satisfactory fit for low- and high-risk patients. Conclusions LR and RF models can effectively predict T2DM risk in the Chinese population. These models may help identify high-risk patients and guide interventions to prevent complications and disabilities.
Currently, precious metal group materials are known as the efficient and widely used oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) catalysts. The exorbitant prices and ...scarcity of the precious metals have stimulated scale exploration of alternative non-precious metal catalysts with low-cost and high performance. Layered double hydroxides (LDHs) are a promising precursor to prepare cost-effective and high-performance catalysts because they possess abundant micropores and nitrogen self-doping after pyrolysis, which can accelerate the electron transfer and serve as active sites for efficient OER. Herein, we developed a new highly active NiFeMn-layered double hydroxide (NFM LDH) based electrocatalyst for OER. Through building NFM hydroxide/oxyhydroxide heterojunction and incorporation of conductive graphene, the prepared NFM LDH-based electrocatalyst delivers a low overpotential of 338 mV at current density of 10 mA cm−2 with a small Tafel slope of 67 mV dec−1, which are superior to those of commercial RuO2 catalyst for OER. The LDH/OOH heterojunction involves strong interfacial coupling, which modulates the local electronic environment and boosts the kinetics of charge transfer. In addition, the high valence Fe3+ and Mn3+ species formed after NaOH treatment provide more active sites and promote the Ni2+ to higher oxidation states during the O2 evolution. Moreover, graphene contributes a lot to the reduction of charge transfer resistance. The combining effects have greatly enhanced the catalytic ability for OER, demonstrating that the synthesized NFM LDH/OOH heterojunction with graphene linkage can be practically applied as a high-performance electrocatalyst for oxygen production via water splitting.
Low-rank representation (LRR) is a classic subspace clustering (SC) algorithm, and many LRR-based methods have been proposed. Generally, LRR-based methods use denoized data as dictionaries for data ...reconstruction purpose. However, the dictionaries used in LRR-based algorithms are fixed, leading to poor clustering performance. In addition, most of these methods assume that the input data are linearly correlated. However, in practice, data are mostly nonlinearly correlated. To address these problems, we propose a novel adaptive kernel dictionary-based LRR (AKDLRR) method for SC. Specifically, to explore nonlinear information, the given data are mapped to the Hilbert space via the kernel technique. The dictionary in AKDLRR is not fixed; it adaptively learns from the data in the kernel space, making AKDLRR robust to noise and yielding good clustering performance. To solve the AKDLRR model, an efficient procedure including an alternative optimization strategy is proposed. In addition, a theoretical analysis of the convergence performance of AKDLRR is presented, which reveals that AKDLRR can converge in at most three iterations under certain conditions. The experimental results show that AKDLRR can achieve the best clustering performance and has excellent speed in comparison with other algorithms.
In recent years, researchers have proposed many graph-based multi-view clustering (GMC) algorithms to solve the multi-view clustering (MVC) problem. However, there are still some limitations in the ...existing GMC algorithm. In these algorithms, a graph is usually constructed to represent the relationship between the samples in a view; however, it cannot represent the relationship very well since it is often preconstructed. In addition, these algorithms ignore the robustness problem of each graph and high-level information between different graphs. Then, in the paper, we propose a novel MVC method, i.e., robust and optimal neighborhood graph learning for MVC (RONGL/MVC). Specifically, we first build an initial graph for each view. However, these initial graphs cannot represent the relationship between the samples in each view well, so we look for the optimal graph with k connected components in the neighborhood of each initial graph, where k is the number of clusters. Then, to improve the robustness of RONGL/MVC, we reconstruct the optimal graph with the self-representation matrix. Furthermore, we stack all the self-representation matrices into a tensor and impose the tensor low-rank constraint, which can maximize consistent features and explore the high-order relationship between optimal graphs. In addition, we provide an optimization strategy by utilizing the Augmented Lagrange Multiplier (ALM) method. Experimental results on several datasets indicate that RONGL/MVC outperforms state-of-the-art methods.
Recently, with the assumption that samples can be reconstructed by themselves, subspace clustering (SC) methods have achieved great success. Generally, SC methods contain some parameters to be tuned, ...and different affinity matrices can obtain with different parameter values. In this paper, for the first time, we study a method for fusing these different affinity matrices to promote clustering performance and provide the corresponding solution from a multi-view clustering (MVC) perspective. That is, we argue that the different affinity matrices are consistent and complementary, which is similar to the fundamental assumption of MVC methods. Based on this observation, in this paper, we use least squares regression (LSR), which is a typical SC method, as an example since it can be efficiently optimized and has shown good clustering performance and we propose a novel robust least squares regression method from an MVC perspective (RLSR/MVCP). Specifically, we first utilize LSR with different parameter values to obtain different affinity matrices. Then, to fully explore the information contained in these different affinity matrices and to remove noise, we further fuse these affinity matrices into a tensor, which is constrained by the tensor low-rank constraint, i.e., the tensor nuclear norm (TNN). The two steps are combined into a framework that is solved by the augmented Lagrange multiplier (ALM) method. The experimental results on several datasets indicate that RLSR/MVCP has very encouraging clustering performance and is superior to state-of-the-art SC methods.
Incomplete multi-view clustering (IMVC) methods have attracted extensive attention in the field of clustering due to their superior performance in addressing incomplete multi-view data. However, ...existing IMVC methods often address balanced incomplete multi-view data, i.e., the missing rate of each view is the same, which does not match reality. In real life, the missing rate of each view in incomplete multi-view data is often different; these are referred to as unbalanced incomplete multi-view data. However, few articles consider the processing of unbalanced incomplete multi-view data. Therefore, we propose an innovative method, unbalanced incomplete multi-view clustering based on low-rank tensor graph learning (UIMVC/LTGL), to handle unbalanced incomplete multi-view data. Specifically, we first use the adjacency relationship between views to adaptively complete similarity graph matrices. To explore the consistency and high-order correlation among views, we further introduce a consensus representation learning term and low-rank tensor constraint into UIMVC/LTGL. In practical applications, each view's contribution to clustering should be different, especially for UIMVC problems. Therefore, we also apply the adaptive weight strategy to each view, which makes reasonable use of the information of each view. The abovementioned steps are integrated into a unified framework to obtain the optimal clustering effect. The augmented Lagrange multiplier (ALM) method is employed to solve the optimization problem. The experimental results on seven well-known datasets fully demonstrate the superiority of the proposed method.
Multi-view clustering is a powerful technique that leverages both consensus and complementary information from multiple perspectives to achieve impressive results. However, existing methods have not ...fully explored the inherent structural information contained in multi-view data, such as inadequately harnessing the consistency among views or neglecting to explore their high-order correlations. To address these limitations, we propose a novel auto-weighted multi-view clustering (AWMVC) method with the use of an augmented view. The main motivation of AWMVC is that each view naturally embodies consensus information, and concatenating the views maximizes the consistency properties. AWMVC first connects multiple views via feature concatenation to form a newly augmented view. Then, we stack the self-representation matrices of the original views and the newly augmented view, i.e., the concatenated view into a 3rd-order tensor, subject to a low-rank tensor constraint. Additionally, this approach automatically assigns weights to each view and constructs a more reliable affinity matrix. Our method unifies the feature concatenation process, high-order information, and a weighted strategy into one model, and is efficiently optimized through an iterative algorithm. Experimental results on benchmark datasets demonstrate that AWMVC outperforms state-of-the-art methods, highlighting the effectiveness of our approach.
•Our method fully explores the inherent structural information in multi-view data.•The augmented view, i.e., concatenated view, maximizes the consistency properties.•Our method adopts an auto-weighted strategy to assign ideal weights to each view.•Experimental results outperform other state-of-the-art clustering methods.
Subspace clustering (SC) methods have attracted widespread attention from interested researchers, and a variety of SC methods have been proposed recently. In general, SC methods obtain an affinity ...matrix, on which spectral clustering is used. Obviously, affinity matrices obtained by different algorithms are not the same, and their clustering effects are also different. A question then arises of whether the affinity matrices obtained by different algorithms can be merged to improve the clustering performance. The answer may be yes I n this paper, we design a new SC method, i.e., robust subspace clustering via multi-affinity matrices fusion (RSC/MAMF). Specifically, several classical SC algorithms, i.e., sparse subspace clustering (SSC), low-rank representation (LRR) and least squares regression (LSR), are first chosen to derive their own affinity matrices. To make full use of the information of different affinity matrices, and to mine the subspace structure thoroughly, we further fuse the derived affinity matrices, i.e., splice them into a 3-order tensor, and impose a weighted tensor nuclear norm (WTNN) to it, which not only mines and fuses the information of the different affinity matrices but also removes their noise. Additionally, to further explore the consistency of the different affinity matrices, spectral embedding is also unified into the final objective function. We propose an optimization algorithm to address the optimization problem of RSC/MAMF, which utilizes the Augmented Lagrange Multiplier (ALM) method. Experiments show that the multi-affinity matrices fusion idea is feasible, and RSC/MAMF outperforms the state-of-the-art SC methods.
