Publications

Improving the Durability of the Wheel Hub Bearings by Utilizing the Sub-zero Treatment Technique

A Weak Signal Detection Method Based on HFER Features in Sea Clutter Background

To address the issue of aliasing between weak signals and sea clutter, we have developed a weak signal detection method leveraging High-Frequency Energy Ratio (HFER) features. This feature detection approach significantly enhances the detection performance of weak signals against the backdrop of sea clutter. By thoroughly examining the echo characteristics that distinguish clutter range gates from target range gates, we transition the analysis from the observation domain to the feature domain, thereby achieving effective discrimination between the two. We analyze the distribution characteristics of high-frequency IMF energy ratios following CEEMD decomposition and construct a weak signal detection network using XGBoost, with the energy ratio as the key feature. The hyperparameters of the network are optimized using the Sparrow Search Algorithm (SSA). We conducted a comparative analysis using the BCD, RAA, TIE, SVM, and multi-feature fusion detection methods. The experimental results showed that the detection probability of the proposed method can reach over 95%, significantly improving the sea surface monitoring and target tracking capabilities of sea radar.

Additional file 3 of Genome-wide identification and expression profiling of auxin response factor (ARF) gene family in maize

Additional file 3:Sequence alignment maize ARF proteins. Clustal_X program were employed to examine sequence features of 31 maize ARF domains. (RTF 3 MB)

Oscillation for Second Order Half-linear Functional Differential Equations

By means of auxilliary functions and Young inequality technique,new oscillation criteria are established for second order half-linear functional differential equations.Its results extend and improve the oscillation theorem of Agarwal R.P.et al.

Effect of measure units on estimating crop LEAF chlorophyll content with remote sensing

Vegetation chlorophyll content is very important for monitoring the growth and health status of vegetation. Remote sensing of chlorophyll content holds an important potential for evaluating crop growth status and diseases and insects. The both units can represent the chlorophyll content of leaves, but there are some differences between them. The former reflects the proportion of chlorophyll in the leaf components, while the latter displays the weight of chlorophyll in the unit leaf area. Combining ten representative hyperspectral vegetation indices, the effect of measurement units on estimating crop leaf chlorophyll content was studied with winter wheat and summer corn leaf chlorophyll content data and leaf reflectance data. First, the relationships between leaf chlorophyll content and the ten selected hyperspectral vegetation indices were analyzed. Second, the estimating models of leaf chlorophyll were built. Finally, the accuracies of estimating models were also assessed based on the validating data. The results show that: (i) due to chlorophyll absorption effect at visible bands, vegetation indices were highly correlated with chlorophyll content of crop leaves and it is feasible to estimate chlorophyll with vegetation index, for example, the relative errors for the estimating model based on MERIS Terrestrial Chlorophyll Index <14%; (ii) The different measurement units of leaf chlorophyll content can give rise to differences in estimating accuracy. Measurement units of mg g−1 and μg cm−2 can both effectively describe chlorophyll content of vegetation leaves. However, the leaf chlorophyll models based on the former were generally superior to the latter. For instance, the relative errors of summer corn for measurement units of mg g−1 and μg cm−2 are 7.6 and 13.6%, respectively. The reflectance signals of leaves at visible and near infrared regions contain various absorption information on leaf components, especially for pigments. Moreover, the measurement unit of mg g−1 reflects the relative content of chlorophyll in the leaf components. Therefore, the unit of mg g−1 is in agreement with the reflectance signals of leaves. However, the unit of μg cm−2 only represents the absolute weight of chlorophyll in the unit leaf area. Therefore, it is recommended that the measurement unit of mg g−1 should be used as far as possible when estimating chlorophyll of crop leaves or other vegetation leaves with remote sensing.

Applications on Information Flow and Biomedical Treatment of FDES Based on Fuzzy Sequential Machines Theory

Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

Temporal Convolutional Network with Attention Mechanisms for Strong Wind Early Warning in High-Speed Railway Systems

High-speed railway (HSR) is a key transport mode for achieving carbon reduction targets and promoting sustainable regional economic development due to its fast, efficient, and low-carbon nature. Accurate wind speed forecasting (WSF) is vital for HSR systems, as it provides future wind conditions that are critical for ensuring safe train operations. Numerous WSF schemes based on deep learning have been proposed. However, accurately forecasting strong wind events remains challenging due to the complex and dynamic nature of wind. In this study, we propose a novel hybrid network architecture, MHSETCN-LSTM, for forecasting strong wind. The MHSETCN-LSTM integrates temporal convolutional networks (TCNs) and long short-term memory networks (LSTMs) to capture both short-term fluctuations and long-term trends in wind behavior. The multi-head squeeze-and-excitation (MHSE) attention mechanism dynamically recalibrates the importance of different aspects of the input sequence, allowing the model to focus on critical time steps, particularly when abrupt wind events occur. In addition to wind speed, we introduce wind direction (WD) to characterize wind behavior due to its impact on the aerodynamic forces acting on trains. To maintain the periodicity of WD, we employ a triangular transform to predict the sine and cosine values of WD, improving the reliability of predictions. Massive experiments are conducted to evaluate the effectiveness of the proposed method based on real-world wind data collected from sensors along the Beijing–Baotou railway. Experimental results demonstrated that our model outperforms state-of-the-art solutions for WSF, achieving a mean-squared error (MSE) of 0.0393, a root-mean-squared error (RMSE) of 0.1982, and a coefficient of determination (R2) of 99.59%. These experimental results validate the efficacy of our proposed model in enhancing the resilience and sustainability of railway infrastructure.Furthermore, the model can be utilized in other wind-sensitive sectors, such as highways, ports, and offshore wind operations. This will further promote the achievement of Sustainable Development Goal 9.

Supervisory Control of PDESs Based on Bisimulation Relations

In this paper, we study supervisory control of a nondeterministic systems -- probabilistic discrete event systems. As language equivalence is not an adequate notion of behavioral equivalence for some nondeterministic systems, here we use the finest known notion of equivalence: bisimulation equivalence. The design of a supervisor such that the controlled system is bisimilar to the specification is studied and a model theorem obtained, in which it shows that a supervisor exists if and only if it exists over a certain finite state space. Also, notions about state-controllability are introduced as part of a necessary and sufficient condition for the existence of the supervisor.

Sea Surface Small Target Detection Integrating OTFS and Deep Unfolding

To address challenges such as sparse feature representation difficulties and poor robustness in detecting weak targets against sea clutter backgrounds, this study investigates the adaptability of channel modeling and sparse reconstruction techniques for target recognition. It proposes a method for detecting small sea targets that integrates OTFS with deep unfolding. Using OTFS modulation to map signals from the time domain to the Delay-Doppler domain, a sparse recovery model is constructed. Deep unfolding is employed to transform the FISTA iterative process into a trainable network architecture. A GAN model is employed for adaptive parameter optimization across layers, while the CBAM mechanism enhances response to critical regions. A multi-stage loss function design and false alarm rate control mechanism improve detection accuracy and interference resistance. Validation using the IPIX dataset yields average detection rates of 88.2%, 91.5%, 90.0%, and 83.3% across four polarization modes, demonstrating the proposed method’s robust performance.

Fuzzy pushdown automata

Sea-Surface Small Target Detection Based on Four Features Extracted by FAST Algorithm

On account of current algorithm and parameter design difficulties and low detection accuracy in feature extractions of small target detections in sea clutter environment, this paper proposes a correspondingly improved four feature extraction method by FAST. After the short-time Fourier transform is applied, a time–frequency distribution spectrogram of original data is generated. Candidate feature points (CFP) are first extracted by FAST algorithm, and then a four-feature extraction is implemented with FAST and DBSCAN combined. The feature distinction is enhanced through a feature optimization. Upon the construction of the four-dimensional feature vectors, XGBoost classifier algorithm classifies and detects these feature vectors. The genetic algorithm optimizes the hyperparameters in XGBoost and updates the decision threshold in real time to control the detection method’s false alarm rate. The IPIX dataset is employed for experimental verification. Verification results confirm that this proposed detection method has better performance than several other currently used detection methods. The detection performance is improved by 7% and 13.8% when observation time is set at 0.512 s and 1.024 s, respectively.

Multimodal Data-Driven Weather Attribute Evaluation and Visualization of Thunderstorm Charge Motion

Comparative analysis of satellite-based and ground-based lightning detection data during 2013-2016 in China

Research on the preparation and performance of Li_2B_4O_7 materials

Maize (<i>Zea mays</i> L.) seedling leaf nuclear proteome and differentially expressed proteins between a hybrid and its parental lines

To better understand the underlying molecular basis of leaf development in maize, a reference map of nuclear proteins in basal region of seedling leaf was established using a combination of 2DE and MALDI-TOF-MS. In total, 441 reproducible protein spots in nuclear proteome of maize leaf basal region were detected with silver staining in a pH range of 3-10, among which 203 spots corresponding to 163 different proteins were identified. As expected, proteins implicated in RNA and protein-associated functions were overrepresented in nuclear proteome. Remarkably, a high percentage (10%) of proteins was identified to be involved in cell division and growth. In addition, comparative nuclear proteomic analysis in leaf basal region of highly heterotic hybrid Mo17/B73 and its parental lines was also performed and 52 of 445 (11.69%) detected protein spots were differentially expressed between the hybrid and its parental lines, among which 16 protein spots displayed nonadditively expressed pattern. These results indicated that hybridization between two parental lines can cause changes in the expression of a variety of nuclear proteins, which may be responsible for the observed leaf size heterosis.

Logistic regression prediction based on fractal characteristics of sea surface targets

Sea clutter is a kind of signal with low signal-to-noise ratio. Fractional Fourier transform is used to gather energy. Fractional Brownian motion model is introduced to model sea clutter. Based on the measured data of IPIX radar, high-order multi-fractal parameters are calculated. Fitting the multi-fractal parameters, choosing the steepest interval and calculating its slope. At the same time, choosing the fractal parameters with scale -30 under HH and VV polarization. The small targets are 0 and 1, respectively. The slope and high-scale fractal parameters of different sea conditions are normalized, and the normalized data are predicted by logistic regression. The simulation results show that FRFT can aggregate the energy of sea clutter signal. Logistic regression model can predict the fractal parameter data of sea clutter FRFT domain, and the prediction accuracy reaches 83.42%.

Artificial-Intelligence-Based Model for Early Strong Wind Warnings for High-Speed Railway System

Wind speed prediction (WSP) provides future wind information and is crucial for ensuring the safety of high-speed railway systems (HSRs). However, the accurate prediction of wind speed (WS) remains a challenge due to the nonstationary and nonlinearity of wind patterns. To address this issue, a novel artificial-intelligence-based WSP model (EE-VMD-TCGRU) is proposed in this paper. EE-VMD-TCGRU combines energy-entropy-guided variational mode decomposition (EE-VMD) with a customized hybrid network, TCGRU, that incorporates a novel loss function: the Gaussian kernel mean square error (GMSE). Initially, the raw WS sequence is decomposed into various frequency-band components using EE-VMD. TCGRU is then applied for each decomposed component to capture both long-term trends and short-term fluctuations. Furthermore, a novel loss function, GMSE, is introduced to the training of TCGRU to analyze the WS’s nonlinear patterns and improve prediction accuracy. Experiments conducted on real-world WS data from the Beijing–Baotou railway demonstrate that EE-VMD-TCGRU outperforms benchmark models, achieving a mean absolute error (MAE) of 0.4986, a mean square error (MSE) of 0.4962, a root mean square error (RMSE) of 0.7044, and a coefficient of determination (R2) of 94.58%. These results prove the efficacy of EE-VMD-TCGRU in ensuring train operation safety under strong wind environments.