Spatiotemporal Evolution Analysis and Future Scenario Prediction of Rocky Desertification in a Subtropical Karst Region

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.

Pedestrian Reidentification Based on Feature Fusion and Metric Learning

The pedestrian recognition in public environment is influenced by the pedestrian environment and the dynamic characteristic boundary factors, so it is easy to produce the tracking error. In order to improve the ability of pedestrian re-identification in public environment, we need to carry out feature fusion and metric learning, and propose pedestrian re-identification based on feature fusion and metric learning. The geometric grid area model of pedestrian recognition in public environment is constructed, the method of fuzzy dynamic feature segmentation is used to reconstruct the dynamic boundary feature point of pedestrian recognition in public environment, the method of bottom-up modeling is used to design the dynamic area grid model of pedestrian recognition in public environment, the design of dynamic area grid model is three-dimensional grid area, the grayscale pixel set of pedestrian recognition dynamic constraint under public environment is extracted, the boundary feature fusion is carried out according to the distribution intensity of grayscale, the image fusion and enhancement information processing of pedestrian recognition under public environment, and the method of 3D dynamic constraint is used to realize the local motion planning of pedestrian recognition under public environment, and the recognition feature fusion and learning of pedestrian recognition under public environment is realized according to the result of contour segmentation. The simulation results show that the method is used for pedestrian recognition again in public environment, and the fuzzy judgment ability of pedestrian dynamic edge features is strong, which makes the error controlled below 10 mm, and the fluctuation of pedestrian recognition again is more stable, the recognition accuracy is higher and the robustness is better.

Network Communication Platform Based on Artificial Intelligence

In order to improve the integration of digital prints, this paper proposes a new method of printmaking analog synthesis, that is, using interactive technology.. Firstly, Harris corner algorithm is used to collect and preprocess the adjacent feature points of digital printmaking image, and the texture features of digital printmaking image are extracted, so as to construct the texture information transmission model of digital printmaking image; Secondly, with the help of segmentation technology to process the digital print image, the dynamic feature segmentation is carried out, the local binary fitting method is used to enhance and repair the digital print image information, and the information fusion method based on interactive technology is used to complete the analog synthesis of digital print image; Finally, the simulation results show that the method has good performance of simulation synthesis and strong information fusion ability.

Numerical Simulation of the Stability of Rock Mass around Large Underground Cavern

Geotechnical problems are complicated to the extent and cannot be expected in other areas since non-uniformities of existing discontinuous, pores in materials and various properties of the components. At present, it is extremely difficult to develop a program for tunnel analysis that considers all complicated factors. However, tunnel analysis has made remarkable growth for the past several years due to the development of numerical analysis method and computer development, given the situation that it was difficult to solve formula of elasticity, viscoelasticity, and plasticity for the dynamic feature of the ground when the constituent laws, yielding conditions of ground materials, geometrical shape and boundary conditions of the structure were simulated in the past. The stability of rock mass around an underground large cavern is the key to the construction of large-scale underground projects. In this paper, the stability analysis was carried out based on those parameters by using 2D FEM RS2 program. The calculated stress and displacements of surrounding rock and rock support by FEM analysis were compared with those allowable values. The pattern of deformation, stress state, and the distribution of plastic areas are analyzed. Finally, the whole stability of surrounding rock mass of underground caverns was evaluated by Rock Science - RS2 software. The calculated axial forces were far below design capacity of rock bolts. The strong rock mass strength and high horizontal to vertical stress ratio enhanced safe working conditions throughout the excavation period. Thus wide span caverns and the system of caverns could be stability excavated sedimentary rock during the underground cavern and the system of caverns excavation by blasting method. The new method provides a reliable way to analyze the stability of the caverns and the system of caverns and also will help to design or optimize the subsequent support. Doi: 10.28991/CEJ-2022-08-01-06 Full Text: PDF

Wire Rope Weak Defect Signal Processing Methods Based on Improved SVD and Phase Space Reconstruction

Due to the complexity of the interference operation environment of wire rope, the detection signals are usually weak and coupled in time-frequency domain, which makes the defect difficult to recognize, while the signal characterizations in phase space are also needed to be studied. Combining the nonlinear dynamic feature identification theories, phase space characteristics and chaotic features of wire rope defect detection signals are mainly investigated in this paper. First, principles of phase space reconstruction method for wire rope detection signals are presented by the chaotic dynamic indexes calculation of embedded dimension and delay time. Second, the change trends of the correlation dimension, approximate entropy and Lyapunov index of different phase space reconstructed wire rope defect detection signals are studied through the nonlinear simulation and analysis. Finally, a phase space reconstruction algorithm based on improved SVD is proposed, and the new algorithm is also compared with traditional signal processing methods. These results obtained by 6 groups of experiments were also evaluated and compared by the parameters of signal-to-noise ratio (SNR) and phase space trajectory chart, which manifests that the improved algorithm not only can increase the weak detection signal SNR to about 2.3dB of wire rope effectively, but also demonstrate the feasibility of the proposed methods in application.

Rotating Instabilities in a Low-Speed Single Compressor Rotor Row with Varying Blade Tip Clearance

When a compressor is throttled to the near stall point, rotating instability (RI) is often observed as significant increases of amplitude within a narrow frequency band which can be regarded as a pre-stall disturbance. In the current study, a single compressor rotor row with varying blade tip clearance (1.3%, 2.6% and 4.3% chord length) was numerically simulated using the zonal large eddy simulation model. The mesh with six blade passages was selected to capture the proper dynamic feature after being validated in comparison to the measured data, and the dynamic mode decomposition (DMD) approach was applied to the numerical temporal snapshots. In the experimental results, RIs are detected in the configurations with middle and large tip gaps (2.6% and 4.3% chord length), and the corresponding characterized frequencies are about 1/2 and 1/3 of the blade passing frequency, respectively. Simulations provide remarkable performance in capturing the measured flow features, and the DMD modes corresponding to the featured RI frequencies are successfully extracted and then visualized. The analysis of DMD results indicates that RI is essentially a presentation of the pressure wave propagating over the blade tip region. The tip leakage vortex stretches to the front part of the adjacent blade and consequently triggers the flow perturbations (waves). The wave influences the pressure distribution, which, in turn, determines the tip leakage flow and finally forms a loop.

Multifeature Metric Learning Based on Enhanced Equidistance Embedding for Electroencephalogram Recognition of Epilepsy

Mobile edge computing (MEC) has the ability of pattern recognition and intelligent processing of real-time data. Electroencephalogram (EEG) is a very important tool in the study of epilepsy. It provides rich information that can not be provided by other physiological methods. In the automatic classification of EEG signals by intelligent algorithms, feature extraction and the establishment of classifiers are both very important steps. Different feature extraction methods, such as time domain, frequency domain, and nonlinear dynamic feature methods, contain independent and diverse specific information. Using multiple forms of features at the same time can improve the accuracy of epilepsy recognition. In this paper, we apply metric learning to epileptic EEG signal recognition. Inspired by the equidistance constrained metric learning algorithm, we propose multifeature metric learning based on enhanced equidistance embedding (MMLE3) for EEG recognition of epilepsy. The MMLE3 algorithm makes use of various forms of EEG features, and the feature weights are adaptively weighted. It is a big advantage that the feature weight vector can be adjusted adaptively, without manual adjustment. The MMLE3 algorithm maximizes the distance between the samples constrained by the cannot-link, and the samples of different classes are transformed into equidistant; meanwhile, MMLE3 minimizes the distance between the data constrained by the must-link, and the samples of the same class are compressed to one point. Under the premise that the various feature classification tasks are consistent, MMLE3 can fully extract the associated and complementary information hidden between the features. The experimental results on the CHB-MIT dataset verify that the MMLE3 algorithm has good generalization performance.

Frequency Contour-Strip Method for Characterization of Damage in Structures under Noisy Conditions

Eigen-frequency, compared with mode shape and damping, is a more practical and reliable dynamic feature to portray structural damage. The frequency contour-line method relying on this feature is a representative method to identify damage in beam-type structures. Although this method has been increasingly applied in the area of damage identification, it has two significant deficiencies: inefficiency in establishing the eigen-frequency panorama; and incompetence to identify cracks in noisy conditions, considerably impairing the effectiveness in identifying structural damage. To overcome these deficiencies, a novel method, termed the frequency contour-strip method, is developed for the first time. This method is derived by extending the frequency contour line of 1D to frequency contour strip of 2D. The advantages of the frequency contour-strip method are twofold: (i) it uses the isosurface function to instantly produce the eigen-frequency panorama with a computational efficiency several orders of magnitude higher than that of the frequency contour-line method; and (ii) it can accommodate the effect of random noise on damage identification, thereby thoroughly overcoming the deficiencies of the frequency contour-line method. With these merits, the frequency contour-strip method can characterize damage in beam-type structures with more efficiency, greater accuracy, and stronger robustness against noise. The proof of concept of the proposed method is performed on an analytical model of a Timoshenko beam bearing a crack and the effectiveness of the method is experimentally validated via crack identification in a steel beam.

On detecting and identifying faulty internet of things devices and outages

As internet of things (IoT) devices play an integral role in our everyday life, it is critical to monitor the health of the IoT devices. However, fault detection in IoT is much more challenging compared with that in traditional wired networks. Traditional observing and polling are not appropriate for detecting faults in resource-constrained IoT devices. Because of the dynamic feature of IoT devices, these detection methods are inadequate for IoT fault detection. In this paper, we propose two methods that can monitor the health status of IoT devices through monitoring the network traffic of these devices. Based on the collected traffic or flow entropy, these methods can determine the health status of IoT devices by comparing captured traffic behavior with normal traffic patterns. Our measurements show that the two methods can effectively detect and identify malfunctioned or defective IoT devices.