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In industrial settings, analyzing vibration signals from rolling bearings is a key method for diagnosing faults in rotating machinery. However, these faults often appear as a series of transient events with quickly changing frequencies, which makes it difficult for traditional time-frequency analysis techniques to accurately capture these characteristics. Methods like synchrosqueezing transmute and multi-synchrosqueezing transmute struggle with this task. In this study, we introduce a new method called the Wavelet-Enhanced Transient-Extracting Transform (WTET). Unlike traditional approaches that rely on instantaneous frequency in a time-domain model, WTET uses a group delay (GD) estimator in a frequency-domain model. This allows for more accurate characterization of bearing and rub impact fault signals. Additionally, WTET is better at resisting noise compared to the traditional wavelet transform because it isolates only the central coefficients in the time-frequency domain. WTET also retains the ability to reconstruct the original signal, which makes it effective at extracting different signal modes. Our analysis shows that WTET offers improved diagnostic capability for detecting bearing faults.

A convolutional concatenation fusion imaging network is proposed for Electrical resistance tomography (ERT) and ultrasonic transmission tomography (UTT) dual-modality fusion imaging, containing four stages: initial imaging, concatenation fusion, feature extraction, and image reconstruction. The fusion of heterogeneous sensors is completed through the initial imaging, and the measured voltage of ERT and the measured sound pressure of UTT are converted into pixel information with the same dimension. The feature information of ERT and UT is sequentially concatenated into the feature vector with the same scale to form a dual-modality shared semantic space, which realizes the fusion of dual-modality feature information. The feature extraction process consists of two convolution blocks and a spatial pyramid pooling block, which completes the self-mining and extraction of multiscale feature. The process of image reconstruction is composed of two deconvolution modules, which complete the reconstruction of multi-medium distribution on the basis of extracted feature. A set of numerical and experimental tests are carried out to evaluate the performance of the proposed method. The results show that the proposed ERT/UTT dual-modality imaging method has obvious improvement in imaging accuracy compared to the dual-modality methods WAF and single-modality methods ERT-CIN and UTT-CIN.

One of the key challenge faced by space-based network is how to maximize the demand for on-board resources for ground communication tasks, given the limited availability of satellite resources. For this challenge, firstly, we propose a joint state space of satellite task requirements and resource pools to obtain the global information of the environment, avoiding convergence to local optimal strategies. Secondly, we propose a new joint partitioning method for frequency and time resources, which avoids the fragmentation of the resource to the maximum extent. Thirdly, a new algorithm called dynamic weight based soft actor critic (DWSAC) is proposed, which enhances the update range when the actions taken by the agent significantly contribute to the improvement of system performance, otherwise weakens the update range, significantly improving the convergence efficiency and performance of the soft actor critic (SAC). The results show that the proposed model and algorithm have good practicability, which can make the average resource occupancy rate higher and the running cost lower.

With China’s electricity spot market about to enter a new stage of “official operation” and large-scale renewable energy connected to the power system, the risk of volatility in spot market prices on the wholesale side of the electricity market is becoming more prominent. To address the issue that the current retail package design of electric power retailers cannot solve the risk of spot electricity price, this paper proposes an optimization approach for retail electricity packages considering customer response., in which the demand response model of retail users and the retail package optimization model are constructed and the DDPG reinforcement learning algorithm is applied to solve the model. Finally, simulation analysis is performed based on historical data and the results show that the optimization method of electricity retail packages taking into account user response effectively mitigates the negative impact of spot electricity price fluctuations and achieves the improvement of economic efficiency; electric power retailers appropriately reduce the pricing level of retail packages to attract new user groups to bring long-term potential revenue and achieve the increase of total revenue; The DDPG reinforcement learning algorithm is used to solve the model, which has an excellent convergence utility and can achieve the dynamic decision-making of electricity retail packages continuously, thereby guiding users to respond promptly.

This study addresses $\mathcal {H}_{\infty }$ filtering against DoS attacks within the framework of singular Markov jump systems. To provide a more comprehensive depiction of the issue pertaining to cyber security, DoS attacks are perceived as occurring in an unpredictable manner and can be represented by a collection of a random variable that follows the Bernoulli distribution. Consequently, the corresponding filter error system is formulated. Utilizing a set of viable linear matrix inequalities, this research establishes criteria of stochastically admissible for filter error system, ensuring a specified $\mathcal {H}_{\infty }$ performance. A filter design method under DoS attacks is proposed. Finally, the effectiveness of this method is demonstrated through a practical example involving a tunnel diode model.

In this article, enhancement-mode (E-mode) operation is achieved by employing a trench gate on the $\beta $ -Ga2O3 current aperture vertical MOSFETs via Sentaurus TCAD simulation. The performance of the trench gate $\beta $ -Ga2O3 MOSFETs was investigated with different structure parameters, such as trench gate depth, channel doping concentration, and drift layer doping concentration and thickness. The transistor with a trench gate recess (Gr) of 130 nm obtained a high on/off current ratio of $10^{8}$ , an on-current density of 65 A/cm2, a low specific on-resistance ( $\text{R}_{\mathrm {on,sp}}$ ) of 42.5 $\text{m}\Omega \cdot $ cm2, and a breakdown voltage (BV) of 488 V for an E-mode operation. With the thickness of the drift layer increasing to $9.2 \mu \text{m}$ , BV is improved to 721 V, $\text{R}_{\mathrm {on,sp}}$ keeps a low value of 43.5 $\text{m}\Omega \cdot $ cm2 due to the trench gate structure, and the transfer characteristic maintains the same. The thermal characteristics of the proposed MOSFET were studied and the device stability with high temperature was analyzed. The transistor with Gr = 130 nm can keep working in E-mode operation at T = 500 K. The performance of the transistor exhibits its great potential as a high-voltage switch device for power electronic applications.

This study presents the biomimetic design of the structure and controller of AutoLEE-II, a self-balancing exoskeleton developed to assist patients in performing multiple rehabilitation movements without crutches or other supporting equipment. Its structural design is founded upon the human body structure, with an eliminated axis deviation and a raised CoM of the exoskeleton. The controller is a physical parameter-independent controller based on the CoM modification. Thus, the exoskeleton can adapt to patients with different physical parameters. Five subjects underwent exoskeleton-assisted rehabilitation training experiments, including squatting, tilting, and walking trainings. The results showed that the exoskeleton can assist patients in completing various rehabilitation exercises and help them maintain their balance during the rehabilitation training. This helpful role of the exoskeleton in rehabilitation training is analyzed through an electromyography (EMG) data analysis. The findings revealed that wearing the exoskeleton can reduce the activity of the lower limb muscles by approximately 20–30% when performing the same rehabilitation exercises.

In cloud computing, multiple workflow scheduling is important to optimize resource allocation and utilization for concurrent execution of diverse workflows across different applications. While previous research has focused on clustering-based resource allocation to reduce communication overheads by grouping tasks, it often overlooks the significance of task execution ordering, limiting overall performance optimization. To address this limitation, we propose two genetic-based approaches, considering task and cluster-level characteristics, to introduce novel ordering techniques for multi-workflow scheduling under cluster-based resource allocation. By comparing two granularity ordering methods, we offer valuable insights for efficient task management in multi-workflow environments. Our experiments demonstrate that the proposed approaches, especially the task granularity-based ordering method, outperform existing primary clustering methods, particularly for scenarios involving a large number of workflows or highly parallel workflows.

The maps used for the path planning of mobile robots are mostly grid maps, which are generated through independent design or sensor measurement to obtain relevant information and then modeling. To obtain the robot motion planning path more quickly, this paper proposes a method of robot motion path planning through the ant colony algorithm under a non-standard environment map. The non-standard environment map is used for standard grid design, and the grid map is optimized by the method of no safety distance added obstacle box selected and safety distance added obstacle box selected, then the path planning is carried out through the ant colony algorithm. In addition, the mutual correspondence between grid maps and real environment maps was solved by adding calibration objects. The experimental results show that this method can not only effectively solve the problem of ant colony algorithms under a non-standard real environment map, show the planning path and pose on the non-standard real environment map, moreover, the safety degree of the planning path in the real environment is also increased by 29.51%, ensuring the safety degree of the whole planning path, which improves the intelligent degree of robot motion path planning.

A large number of the WRLSs (wearable robots lumbar support) research have been presented for working efficient increase and injure risk reduction in recent years. However, the previous research can only complete the sagittal-plane lifting task, which can not adapt to the mixed lifting tasks in the actual work scene. Therefore, we presented a novel lumbar assisted exoskeleton with mixed lifting tasks by various postures based on position control, which can not only carry out the lifting tasks of sagittal-plane, but also complete the lifting tasks of sides. First, we proposed a new generation method of raising reference curves that can generate assistance curve for each user with each task, which is very convenient in mixed lifting tasks. Then, an adaptive predictive controller was designed to track the reference curves of different users under different loads, the maximum tracking errors of the angles are ${2}.{2}^{\circ }$ and ${3}.{3}^{\circ }$ respectively at 5kg and 15kg, and all the errors are within 3%. Compared to the condition of no exoskeleton, the average RMS (root mean square) of EMG (electromyography) for six muscles are reduced by $10.33\pm 1.44\%$ , $9.62\pm 0.69\%$ , $10.97\pm 0.81\%$ and $14.48\pm 2.11\%$ by lifting loads with stoop, squat, left-asymmetric and right-asymmetric respectively. The results demonstrate that our lumbar assisted exoskeleton presents outperformance in mixed lifting tasks by various postures.

Multi-satellite joint regional coverage aims to select the optimal combination of satellite resources to acquire the image information of the specified area. Meanwhile, more than three objectives are usually considered simultaneously during this process. Therefore, it is a typical many-objective optimization problem that is NP-hard. Most existing many-objective optimization algorithms cannot preserve extreme solutions due to the failure of Pareto dominance. In this paper, through introducing the idea of S-CDAS into the traditional NSGA-III, an improved many-objective evolutionary algorithm named NSGA-III for extreme solutions preservation (ESP-NSGA-III) is proposed with problem-specific genetic operations to generate regional coverage schemes. A comparative study is conducted with other six state-of-the-art many-objective evolutionary algorithms. Hypervolume (HV) and pure diversity (PD) metrics are used to evaluate the performance of algorithms. The simulation results show that ESP-NSGA-III has good comprehensive performance and improves the diversity of original algorithms. The maximum difference of the coverage rate between ESP-NSGA-III and other six algorithms is 0.2576 so that satisfactory regional coverage scheme can be obtained by ESP-NSGA-III. Our proposed methods are not only applicable to regional coverage tasks, but also have important reference significance for solving other real-world problems.

Based on the modified gravity model and social network analysis method, the evolution characteristics and driving mechanism of logistics network spatial structure in the Zhengzhou metropolitan area (ZMA) from 2008 to 2019 are analyzed at a county (district) scale. Research findings reveal that the following: (1) The distribution of logistics links in ZMA counties (districts) has significant spatial heterogeneity, with a spatial pattern of “sparse in the east and dense in the west,” and the counties (districts) logistics links are concentrated in Zhengzhou. (2) The overall level of the logistics network density in ZMA is low, but it is fluctuating and rising. The overall realization is the logistics radiation effect. The logistics network layout’s core area is primarily concentrated in Zhengzhou’s main urban area and surrounding counties (districts), and the spatial scope is expanding and merging into one piece. The contact density of counties (districts) in the core area is high, whereas that between counties (districts) in the edge area is low. (3) With obvious spatial correlation characteristics, the logistics connection intensity of counties (districts) in ZMA plays a positive role in promoting the logistics connection intensity of surrounding areas. (4) The main factors that promote the formation of the local logistics network structure are the upgrading of industrial structure (ln Ind), the level of economic development (ln GDP), and the size of the population (ln Pop). The ln Ind and ln GDP restrain the logistics network structure and internal logistics network structure in the surrounding areas, whereas the ln Pop and government intervention (ln Gov) promote the formation of the external logistics network structure in the surrounding areas.

Complex-valued shift-invariant canonical polyadic decomposition (CPD) under a spatial phase sparsity constraint (pcsCPD) shows excellent separation performance when applied to band-pass filtered complex-valued multi-subject fMRI data. However, some useful information may also be eliminated when using a band-pass filter to suppress unwanted noise. As such, we propose an alternating rank- ${R}$ and rank-1 least squares optimization to relax the CPD model. Based upon this optimization method, we present a novel constrained CPD algorithm with temporal shift-invariance and spatial sparsity and orthonormality constraints. More specifically, four steps are conducted until convergence for each iteration of the proposed algorithm: 1) use rank- ${R}$ least-squares fit under spatial phase sparsity constraint to update shared spatial maps after phase de-ambiguity; 2) use orthonormality constraint to minimize the cross-talk between shared spatial maps; 3) update the aggregating mixing matrix using rank- ${R}$ least-squares fit; 4) utilize shift-invariant rank-1 least-squares on a series of rank-1 matrices reconstructed by each column of the aggregating mixing matrix to update shared time courses, and subject-specific time delays and intensities. The experimental results of simulated and actual complex-valued fMRI data show that the proposed algorithm improves the estimates for task-related sensorimotor and auditory networks, compared to pcsCPD and tensorial spatial ICA. The proposed alternating rank- ${R}$ and rank-1 least squares optimization is also flexible to improve CPD-related algorithm using alternating least squares.

The brightness of low-temperature polycrystalline silicon and metal-oxide (LTPO) active-matrix organic light-emitting diode (AMOLED) display under low-refresh-rate driving condition tends to increase during pixel voltage holding time of each frame even though there is no critical leakage current path from the storage capacitor. We have found that the main cause of the brightness increase is electron trapping into the gate insulator of driving thin-film transistor (DTFT) which operates in the saturation mode prone to hot carrier injection. We propose a novel driving method to suppress the brightness variation by applying a high voltage to the source and drain of the DTFT during the non-emitting time which is inserted periodically for dimming the organic light-emitting diode (OLED) display. Our experimental results show that the brightness variation can be reduced from 3.7 % to 1.4 % at the 63rd gray level by the proposed DTFT reset scheme.

Electrical Resistance Tomography (ERT) is a promising technique owing to its low cost, high speed, and visualization features. The sensitivity coefficient plays a significant role in ERT image reconstruction as a priori information. However, the existing methods for calculating sensitivity matrix suffer from poor interpretability and operability. The result leads to an obstacle to improving the quality of the reconstructed images. Therefore, we propose a new sensitivity calculation method that has the advantages of clear physical significance, high robustness, and strong interpretability. We compared the existing and new sensitivity matrices with some metrics, such as correlation coefficient and relative error, and tested the new method’s performance by numerical simulations and experiments. The results show that the new sensitivity matrix is feasible and has better performance in ERT imaging reconstruction, especially in the presence of noise.

Vehicle exhaust prediction is of great importance for exhaust emissions control and public environment protection, and very challenging because it is influenced by various complex factors, including multi-component temporal dependencies (closeness, period, trend) and external factors, e.g., meteorological data and workday information. To address these issues, we propose a novel deep-learning-based framework, called multi-component fusion temporal networks (MCFT-Net), to collectively forecast emission concentration of carbon monoxide (CO). The proposed MCFT-Net consists of three parts, 1) a one-dimensional Convolution Neural Networks (1D-CNNs) module to capture temporal dependencies of vehicle emissions by merging closeness, period, and trend time sequences; 2) an artificial neural network (ANN) module to capture features of external factors, such as wind direction, weekday and weekend; and 3) a cascade fusion component to merge the temporal patterns, which are derived from temporal dependencies processed by multiple 1D-CNNs, and then assigned different weights according to an attention mechanism after fusion. The output of the attention operation is further combined with external features through sum and average operations to predict the final emission concentration. We evaluate the proposed MCFT-Net using two real-world vehicle exhaust datasets, ExhaustBMS and ExhaustHMS, which were collected from Beijing monitoring station (BMS) and Hefei monitoring station (HMS). The results demonstrate that our MCFT-Net outperforms 8 baseline methods, such as ARIMA, RNN, GRU, BiLSTM.

The internet is the home for huge volumes of useful data that is constantly being created making it difficult for users to find information relevant to them. Recommendation System is a special type of information filtering system adapted by online vendors to provide recommendations to their customers based on their requirements. Collaborative filtering is one of the most widely used recommendation systems; unfortunately, it is prone to shilling/profile injection attacks. Such attacks alter the recommendation process to promote or demote a particular product. Over the years, multiple attack models and detection techniques have been developed to mitigate the problem. This paper aims to be a comprehensive survey of the shilling attack models, detection attributes, and detection algorithms. Additionally, we unravel and classify the intrinsic traits of the injected profiles that are exploited by the detection algorithms, which has not been explored in previous works. We also briefly discuss recent works in the development of robust algorithms that alleviate the impact of shilling attacks, attacks on multi-criteria systems, and intrinsic feedback based collaborative filtering methods.

This paper is aimed at the telemetry data of Luojia1-01 satellite attitude control system, and carries out the research on high-dimensional data feature extraction and fault detection based on supervised local linear embedding (SLLE). Because the general linear feature extraction method can not mine the feature information of nonlinear high-dimensional telemetry data, a data feature extraction and fault detection method based on local linear embedding is designed. Combined with the statistics SPE and $T^{2}$ , the low-dimensional feature information is obtained for data statistics and monitoring faults. Due to the local linear embedded manifold learning method for the traditional batch processing mode is difficult to update and improve the database online, and the supervised local linear embedding method is introduced. The online sample feature extraction and fault detection schemes are designed, and the database is updated by updating the weight matrix through online samples. Finally, the effectiveness of the method is verified by Luojia1-01 satellite telemetry data. The results show that the fault detection method based on SLLE reduces the false alarm rate (FAR) by approximately 3% and the missing alarm rate (MAR) by approximately 10% compared with local linear embedding (LLE). This method effectively improves the detection capability of the anomalous state of Luojia1-01 satellite and has certain engineering application value.