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.
Abstract In this work, we analyzed the influence of lightning electromagnetic impulses on the underground coaxial cables through environmental simulation and got such conclusions. 1) The coupling voltage is proportional to the length of the underground coaxial cable. 2) The coupling voltage is inversely proportional to the depth of the underground coaxial cable. 3) The coupling voltage is proportional to the value of the impulsive voltage. These discoveries are of great application value for lightning protection techniques of coaxial cables.
The microtremor survey method (MSM) holds great potential for obtaining subsurface shear wave velocity structures in exploration geophysics. However, the lack of an instant imaging mechanism with local fast computation and processing has become a significant bottleneck hindering the development of MSM. In instant imaging tasks, the computational resources of ordinary nodes employed for imaging are often limited. In this paper, we consider a single-point microtremor array network with time-varying wireless channels and stochastic imaging task data arrivals in sequential time frames. In particular, we aim to design an online computation offloading algorithm to maximize the network data processing capability and optimize service quality subject to the long-term data queue stability and average power constraints. We formulate the problem as a the minimum delay problem that jointly determines the binary offloading and system resource allocation decisions in sequential time frames. To address the coupling in the decisions of different time frames, we propose a novel framework named LyECCO that combines the Lyapunov optimization and energy consumption optimization, solve the binary offloading problems with very low computational complexity. Simulation results show the feasibility of the LyECCO, which achieves optimal computation performance while stabilizing all queues in the system.
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.
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.
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)
Global horizontal irradiance (GHI) measured with rotating shadowband radiometer (RSR) is not accurate enough due to thermal sensitivity and nonuniform spectral response of the photovoltaic detector equipped inside. The purpose of this work is to develop a multiple regressive model to correct the errors posed by the temperature and spectrum. The ratio of the reference global horizontal irradiance (RGHI) to the RSR measured GHI is defined as correction factor, based on which, the model is built via device temperature, air mass, and solar zenith angle. Evaluated from various statistical tests such as coefficient of correlation R2, mean bias deviation, root mean square deviation, t-statistic, skewness, and kurtosis, results show that the corrected RSR GHI can be comparable with the high-quality RGHI, which indicates the validity of the model.
The strong clutter induced by stationary and slow moving tissue structures may distort the low frequency components of spectrogram and degrade the precision of clinical indices. A new wavelet method, the double density discrete wavelet transform (DD-DWT) combining the semisoft shrinkage function and a threshold with local variance estimation, is proposed for removing adaptively clutter components from Doppler ultrasound signals. The new method is tested with both simulated and in vivo Doppler blood signals, and compared with conventional high pass filter (HPF). The improvements in sonogram and signal-to-clutter (S/C) ratio of Doppler signals filtered by new method over HPF are noticeable. For the simulated signal with S/C ratio -20 dB, the improvement in S/C ratio is 22.51 dB by new method, but 22.05 dB by HPF. The low frequency part of resultant sonograms verifies that the new method can effectively remove clutter components and retain more low flow signal simultaneously.
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.
By using Riccati's methods and Young inequality, new oscillation theorems are established for second order half-linear retarded differential equations.
In response to the challenges in the realm of intrusion detection for marine meteorological sensor networks, such as difficulties in model training, inadequate detection performance, and low operational efficiency, we propose an advanced intrusion detection model. This model leverages feature dimensionality reduction, utilizing the Genetic Algorithm based on Random Forest (GARF) technique, to discern the most effective feature subset, thereby streamlining the original network intrusion detection dataset. An Approximate Nearest Neighbor (ANN) algorithm is employed to convert network traffic data into a graph structure, and the Approximate Nearest Neighbor-based Graph Convolutional Neural Network (AGCN) is constructed for traffic classification prediction on this graph-structured data. Our simulation experiments conducted on the NSL-KDD dataset have yielded positive results, demonstrating the model’s enhanced intrusion detection capabilities and efficiency, and affirming its potential to provide robust security measures for marine meteorological sensor networks.
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.
