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The fault diagnosis of building electrical systems are of great significance to the safe and stable operation of modern intelligent buildings. In this paper, it has many problems, such as various fault types, inconspicuous fault characteristics, uncertainty of fault type and mode, irregularity, unstable signal, large gap between fault data classes, small gap between classes and nonlinearity, etc. A method of building electrical system fault diagnosis based on the combination of variational mode decomposition and mutual dimensionless indictor (VMD-MDI) and quantum genetic algorithm-support vector machine (QGA-SVM) is proposed. Firstly, the method decomposes the original signal through variational modal decomposition to obtain the optimal number of Intrinsic Mode Function(IMF) containing fault feature information. Secondly, extracts the mutual dimensionless indicator for each IMF. Thirdly, the optimal penalty coefficient C of the support vector machine and the parameter gamma (γ ) in the radial basis kernel function are selected by the quantum genetic algorithm. Finally, SVM optimized by the QGA is used to identify and classify the faults. By applying the proposed method to the experimental platform data of building electrical system, and compared with the traditional feature extraction method Empirical Mode Decomposition (EMD), Singular Value Decomposition(SVD), Local Mean Decomposition(LMD). And compared with traditional SVM, Genetic Algorithm optimized Support Vector Machine (GA-SVM), One-Dimensional Convolutional Neural Network (1DCNN) for fault classification methods. The experimental results show that the method has better effect and higher accuracy in fault diagnosis and classification of building electrical system. Its average test accuracy can reach 91.67 % . [ABSTRACT FROM AUTHOR]

This paper focuses on the finite-time generalized synchronization problem of non-identical fractional order chaotic (or hyper-chaotic) systems by a designing adaptive sliding mode controller and its application to secure communication. The effects of both disturbances and model uncertainties are taken into account. A novel fractional order integral sliding mode surface is designed and its stability to the origin is proved in a given finite time. By the aid of the fractional Lyapunov stability theory, a robust controller with adaptive update laws is proposed and its finite-time stability for generalized synchronization between two non-identical fractional-order chaotic systems in the presence of model uncertainties and external disturbances is derived. Numerical simulations are provided to demonstrate the effectiveness and robustness of the presented approach. All simulation results obtained are in good agreement with the theoretical analysis. According to the proposed generalized finite-time synchronization criterion, a novel speech cryptosystem is proposed to send or share voice messages privately via secure channel. Security and performance analyses are given to show the practical effect of the proposed theories. [ABSTRACT FROM AUTHOR]

Pupil localization is one of the most critical and essential requirements for eye gaze estimation and eye movement tracking. Because pupil images contain monotonous and uncomplicated information, the dataset uses a single class of labels to describe the image content, and using convolutional neural networks can quickly and accurately identify the pupil position on the input image. On low-resolution images, traditional methods encounter issues of low accuracy and cumbersome design steps. A lightweight pupil localization algorithm is proposed in this paper, utilizing a convolutional neural network (CNN) with additional training samples. The experimental results demonstrate the algorithm’s significant effectiveness in identifying the pupil position within the training set, with the accuracy of pupil position in the test set reaching 97.78%. This provides an evidence of the algorithm’s feasibility for accurately localizing pupils in low-resolution images. [ABSTRACT FROM AUTHOR]

In this paper, an image enhancement algorithm is presented for identification of corrosion areas and dealing with low contrast present in shadow areas of an image. This algorithm uses histogram equalization processing under the hue-saturation-intensity model. First of all, an etched image is transformed from red-green-blue color space to hue-saturation-intensity color space, and only the luminance component is enhanced. Then, part of the enhanced image is combined with the original tone component, followed by saturation and conversion to red-green-blue color space to obtain the enhanced corrosion image. Experimental results show that the proposed method significantly improves overall brightness, increases contrast details in shadow areas, and strengthens identification of corrosion areas in the image. [ABSTRACT FROM AUTHOR]

Evidence reasoning (ER) combined with dimensionless index method can be used in rotating machinery fault diagnosis. In ER algorithm, reliability is mainly obtained in two ways: distance-based method and correlation measure by set theory. In practice, the distance-based method cannot generate high-discrimination reliability in high-coincidence data like dimensionless index data. Therefore, correlation measure by set theory method is used in fault diagnosis more frequently. Because correlation measure by set theory only considers upper bound and lower bound of fault data, we add a regularization term to calculate the relationship between the inner data. Experience result shows that fault diagnosis accuracy had improved, which illustrates that the new reliability can describe data relationship better. [ABSTRACT FROM AUTHOR]

Overstudy or understudy phenomena can sometimes occur due to the strong dependence of support vector machine (SVM) algorithms on particular parameters and the lack of systems theory relating to parameter selection. In this paper, a parameter optimization algorithm for the SVM is proposed based on multi-genetic algorithm. The algorithm optimizes the correlation kernel parameters of the SVM using evolutionary search principles of multiple swarm genetic algorithms to obtain a superior SVM prediction model. The experimental results demonstrate that by combining the genetic algorithm and SVM algorithm, fault diagnosis can be effectively realized for bearings of rotating machinery. [ABSTRACT FROM AUTHOR]

The drivetrain has an important impact on the stability and reliability of wind turbines operating in complex conditions, which belongs to the electromechanical coupling system. This work studies the two domains of research on drivetrain vibration analysis and control method in detail. The electromechanically coupled torsional vibration model is first established by considering the air gap magnetic field energy of the PMSG. Preliminary analysis in terms of Hamiltonian energy indicates that there is sufficient energy to sustain the exciting oscillation behaviors. Moreover, to reduce the vibration amplitude caused by wind speed or torsional stiffness, a novel adaptive fixed‐time control method is proposed to solve the issue of wind turbine drivetrain chaotic oscillation with uncertainties and disturbances. Furthermore, the proposed method not only can prevent the jumping and bifurcation of torsional vibration from happening, but also there are the advantages of the control scheme with high accuracy, fast convergence rate and strong robustness. For the fair comparison, three different control methods have been used to demonstrate the superiority of the control scheme by the simulation results. The research results can provide a theoretical basis for the parameter design and control of wind turbine drivetrain. [ABSTRACT FROM AUTHOR]

It is difficult to well distinguish the dimensionless indexes between normal petrochemical rotating machinery equipment and those with complex faults. When the conflict of evidence is too big, it will result in uncertainty of diagnosis. This paper presents a diagnosis method for rotation machinery fault based on dimensionless indexes combined with K-nearest neighbor (KNN) algorithm. This method uses a KNN algorithm and an evidence fusion theoretical formula to process fuzzy data, incomplete data, and accurate data. This method can transfer the signals from the petrochemical rotating machinery sensors to the reliability manners using dimensionless indexes and KNN algorithm. The input information is further integrated by an evidence synthesis formula to get the final data. The type of fault will be decided based on these data. The experimental results show that the proposed method can integrate data to provide a more reliable and reasonable result, thereby reducing the decision risk. [ABSTRACT FROM AUTHOR]

The tumor immune microenvironment has manifested a crucial correlation with tumor occurrence, development, recurrence, and metastasis. To explore the mechanisms intrinsic to osteosarcoma (OS) initiation and progression, this study synthesizes multiple single‐cell RNA sequencing data sets, constructing a comprehensive landscape of the OS microenvironment. Integrating single‐cell RNA sequencing with bulk RNA sequencing data has enabled the identification of a significant correlation between heightened expression of the fatty acid metabolism‐associated gene (C1QBP) and patient survival in OS. C1QBP not only amplifies the proliferation, migration, invasion, and anti‐apoptotic properties of OS but also instigates cisplatin resistance. Subsequent investigations suggest that C1QBP potentially promotes macrophage polarization from monocytes/macrophages toward M2 and M3 phenotypes. Consequently, C1QBP may emerge as a novel target for modulating OS progression and resistance therapy. [ABSTRACT FROM AUTHOR]

Ferroresonance is characterized by overvoltage and irregular operation in power systems, which can greatly endanger system equipment. Mechanism analysis of the ferroresonance phenomenon depends mainly on model accuracy. Due to the fractional-order characteristics of capacitance and inductance, fractional-order models are more universal and accurate than integer-order models. A typical 110 kV ferroresonance model is first established. The influence of the excitation amplitude on the dynamic behavior is analyzed. The fractional-order ferroresonance model is then introduced, and the effects of the fractional order and flux-chain order on the system's motion state are studied via bifurcation diagrams and phase portraits. In order to suppress the nonlinear dynamic behavior of fractional-order ferroresonance systems, a novel fractional-order fast terminal sliding mode control method based on finite-time theory and the frequency distributed model is proposed. A new fractional-order sliding mode surface and control law using a saturation function are developed. A robust fractional-order sliding mode controller could achieve finite-time stabilization and tracking despite model uncertainties and external disturbances. Compared with conventional sliding mode methods, the simulation results highlight the effectiveness and superiority. The research provides a theoretical basis for ferroresonant analysis and suppression in large-scale interconnected power grids. [ABSTRACT FROM AUTHOR]

As mobile communication technology continues to develop, millimeter-wave communication for 5G mobile networks is attracting widespread public attention. Many studies have appeared in the literatures related to millimeter wave mobile broadband communication systems. This paper briefly introduces the development of 5G and millimeter wave communications, analyses several practical designs and implementations of millimeter wave communications for 5G, and proposes some future applications and challenges for 5G. [ABSTRACT FROM AUTHOR]

It is challenging to control and optimize the aluminum electrolysis process due to its non-linearity and high energy consumption. Reducing the cell voltage is crucial for energy consumption reduction. This paper presents an intelligent method of predicting and optimizing cell voltage based on the evaluation of modeling the comprehensive cell state. Firstly, the Savitzky–Golay filtering algorithm(SGFA) is adopted to denoise the sample data to improve the accuracy of the experimental model. Due to the influencing factors of the cell state, a comprehensive evaluation model of the cell state is established. Secondly, the model of the least squares supports vector machine (LSSVM) is proposed to predict the cell voltage. In order to improve the accuracy of the model, the state transition algorithm (STA) is employed to optimize the structure parameters of the model. Thirdly, the optimization and control model of the cell voltage is developed by an analysis of the technical conditions. Then, the STA is used to realize the optimization of the front model. Finally, the actual data were applied to the experiments of the above method, and the proposed STA was compared with other methods. The results of experiments show that this method is efficient and satisfactory. The optimization value of average cell voltage based on the STA-LSSVM is 3.8165v, and it can be used to guide process operation. The DC power consumption is 11,971 KW·h per tonne of aluminum, with a reduction in power consumption of 373 KW·h. This result guarantees the reduction of aluminum electrolysis energy consumption. [ABSTRACT FROM AUTHOR]

Efficient and accurate diagnosis of rotating machinery in the petrochemical industry is crucial for ensuring normal machinery operation. However, the nonlinear and non-stationary vibration signals generated by machinery in harsh environments pose significant challenges in distinguishing fault signals from normal ones. Although several fault diagnosis methods based on mutual dimensionless indicators (MDI) have been proposed, they often fail to achieve effective and accurate health monitoring. Hence, this paper proposes a BP-Incremental Broad Learning System (BP-INBLS) model based on the combined synergistic of two modules, to address the existing challenges. Firstly, a new mutual dimensionless indicator (VMDI) with high sensitivity and low overlap is refactored. Secondly, leveraging the advantages of incremental learning algorithms, a novel Broad Learning System (BLS) model for quickly identifying different fault types is constructed. Finally, the proposed method is validated using multiple datasets and verified through a comparative analysis with a published method based on dimensionless indicators (DI). The results demonstrate the effectiveness of the proposed method in fault diagnosis. [ABSTRACT FROM AUTHOR]

Recently multi-task feature learning has become a widely applied approach for visual tracking, since it is benefited from the shared features across tasks. However, selecting features appropriately from multiple tasks is still a challenging problem due to the complex variation of the appearance of moving objects, which influences not only the features of single task but also the relationships between the features of multiple tasks. To address this problem, this paper presents a novel sparse learning model for selecting multi-task features adaptively. Compared to the existing multi-task models, the proposed model is capable of both calibrating the loss function according to the noise level of a task to keep its specific features, and identifying the relevant and irrelevant (outlier) tasks simultaneously by decomposing the regularized matrix into two specified structures. The proposed model allows to preserve specific features of individual tasks via calibration and to exploit sparse pattern over the relevant task via identification. Empirical evaluations demonstrate that the proposed method has better performance than a number of the state-of-the-art trackers on available public image sequences. [ABSTRACT FROM AUTHOR]