Simulation Experimental Results Research Articles

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.

A successive variational mode extraction method based on spectrum trend and its application in non‐contact vital sign detection

AbstractDetecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies.

Experimental and numerical simulation of the attenuation effect of blast shock waves in tunnels at different altitudes

Traffic engineering such as tunnels in various altitudinal gradient zone are at risk of accidental explosion, which can damage personnel and equipment. Accurate prediction of the distribution pattern of explosive loads and shock wave propagation process in semi-enclosed structures at various altitude environment is key research focus in the fields of explosion shock and fluid dynamics. The effect of altitude on the propagation of shock waves in tunnels was investigated by conducting explosion test and numerical simulation. Based on the experimental and numerical simulation results, a prediction model for the attenuation of the peak overpressure of tunnel shock waves at different altitudes was established. The results showed that the peak overpressure decreased at the same measurement points in the tunnel entrance under the high altitude condition. In contrast, an increase in altitude accelerated the propagation speed of the shock wave in the tunnel. The average error between the peak shock wave overpressure obtained using the overpressure prediction formula and the measured test data was less than 15%, the average error between the propagation velocity of shock waves predicted values and the test data is less than 10%. The method can effectively predict the overpressure attenuation of blast wave in tunnel at various altitudes.

Numerical simulation of ultrasonic P-wave propagation in water-bearing coal based on gas-liquid homogeneous wave velocity model

This paper establishes the equivalent density and equivalent P-wave velocity models of coal bodies with different water saturation, and investigates the P-wave propagation evolution law of coal bodies with different water saturation from the perspective of numerical simulation. When the experimental coal samples continue to use high-pressure water after reaching the natural saturation state, the water saturation can be further increased, and the increase in water saturation is larger, and the corresponding P-wave velocity also appears to be increased more substantially, and this phenomenon also occurs in numerical simulation results, which indicates that the P-wave velocity will have an obvious increasing trend when the coal body is from nearly saturated to fully saturated. The experimental and numerical simulation results show that the P-wave velocity of coal samples increases with the water saturation degree in a similar exponential function, which further verifies the reasonableness and feasibility of the modelling based on the assumption of gas-liquid homogeneous medium. In addition, the propagation of P-wave in coal samples is affected by the pore and fracture structure, and the pore and fracture with small pore size has less influence on the P-wave, and vice versa.

Research on channel modeling technology of visible light communication system based on the ray tracing method

Abstract Visible Light Communication (VLC) is a new mode that combines lighting and communication. Because it can meet the needs of large capacity and ultra-high transmission rate, effective analysis of indoor visible light channel characteristics can better provide VLC systems. We provide a reference basis for the design, and VLC technology using Light Emitting Diode (LED) as the light source has become one of the research hotspots in the field of wireless communications. To solve the problems of existing indoor visible light channel modeling methods such as being ideal, high complexity, or poor in versatility, this paper researches indoor visible light communication channel modeling and related theories and methods, using rays with strong scene adaptability. Based on the tracking method, starting from the need to reduce computational complexity and improve tracking accuracy, a channel modeling method for indoor visible light communication is designed. We propose a high-precision indoor visible light communication channel modeling method that combines central ray tubes and bidirectional ray tracing. We use the forward ray tracing method to model indoor visible light communication channels. The simulation experimental results show that the channel modeling method proposed in this article can effectively improve modeling efficiency.

Tensor Ring Decomposition Guided Dictionary Learning for OCT Image Denoising.

Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

Research on laser communication ranging Integration and VLC nonlinear compensation technology

Abstract Visible light communication (VLC) is a new spectrum communication technology that provides a new direction to solve spectrum resource problems. The nonlinearity problem in VLC will lead to a significant reduction in the performance of the communication system. To solve this problem, this paper studies the integration of laser communication ranging and VLC nonlinear compensation technology based on analyzing the principles of ranging methods and factors affecting ranging performance. Aiming at the problem that the complexity of the Random Fourier Features Kernel adaptive filter (RFF-KAF) method will increase significantly as the dimension of the mapping space increases, we introduce some second-order Volterra series square terms into the input vector of RFF-KAF and propose an improved prediction method. Experimental simulation results show that the method proposed in this article can greatly improve the performance of the RFF-KAF method while maintaining the same mapping space dimension. Compared with obtaining similar performance by increasing the mapping space dimension, the cost of this method is greatly reduced.

Behavioral propagation based on regret-hesitation psychology on multiple networks

There are many factors affecting the social propagation of adoption behavior, and the traditional propagation model is not enough to study the influence of human psychological factors. We introduce the psychological mechanism of regret and hesitation to explain the complexity of individual decision making and information propagation. In this paper, a method based on edge division theory is used to analyze the propagation process on multiple networks, and the experimental results show the phenomenon of mixed phase transition. When the system is dominated by regret psychology, the system can achieve global eruption, but when it is dominated by hesitation psychology, the system cannot achieve global eruption. The results of theoretical analysis and experimental simulation agree well. It is of great significance to the study of sociology, psychology and information propagation.

Microstructure and finite element analysis of Mo2C-diamond/Cu composites by spark plasma sintering

Abstract Mo2C layer was generated on the diamond surface via vacuum micro-evaporating, which was used as the reinforcement particles to fabricate diamond/Cu composites by spark plasma sintering (SPS). The effect of evaporation parameters on the forming of Mo2C, and the holding time on diamond/Cu composites fabrication is studied. Combined with the experiment and finite element analysis (FEA), the holding time on diamond/Cu composites influence on the thermal conductivity (TC) of composites is further discussed. The results show that the Mo2C area on the diamond surface would gradually enlarge and cover the diamond surface evenly with the increment in evaporation time and temperature, better vacuum micro-evaporating parameters were given as 1,000°C for 60 min. The fractures in the diamond/Cu composites are mainly ductile fractures on copper and diamond falling out from the Mo2C interface. It was found that sintering time would significantly influence the dissipation property of diamond/Cu composites. A comprehensive parameter for SPS was obtained at 900°C, 80 MPa for 10 min, the relative density (RD) and TC of the composites obtained under the parameter were 96.13% and 511 W/(m K). A longer sintering time would damage the Mo2C interlayer and further decrease the bonding between copper matrix and diamond particles, which would lower the RD and TC of composites. It can be obtained from the comparison of simulation results and experimental results that the FEA result is closer to the experimental results due to the gaps with low heat conduction, and the air in the gaps is added in the simulation process.

Multi-strategy Improved Pelican Optimization Algorithm for Mobile Robot Path Planning

In response to the problems of easily falling into local optima, low path planning accuracy, and slow convergence speed when applying the traditional pelican optimization algorithm to the mobile robot path planning problem, a multi-strategy improved pelican optimization algorithm (MPOA) is proposed. In the initialization stage, chaotic mapping is used to increase the diversity of the pelican population individuals. In the exploration stage, an adaptive feedback adjustment factor is proposed to adjust the local optima of pelican individuals’ positions and balance the algorithm’s local development capability. In the development stage, the Lévy flight strategy is introduced to adjust the domain radius of the pelican population individuals, and the Gaussian mutation mechanism is used to disturb individuals that have fallen into local optima. Simulation experimental results show that the improved algorithm has significantly improved and effectively shortened the length of the planned path.

Study on Multi-Scale Cloud Growth Characteristics of Frustoconical Dispersal Devices

This study aims to understand cloud growth behavior and enhance cloud safety and reliability by investigating the design of cloud dispersal devices. Based on the experimental results and simulation results, this study analyzes the dispersion characteristics of cloud materials within a frustoconical device with a semi-cone angle ranging from 0° to 10° across multiple scales. The collision aggregation model for cloud particles and the multi-scale coupling mechanism for cloud growth are established. The research shows that the semi-cone angle of the device extends the effective cloud growth duration and enlarges the cloud macroscopic size. At the mesoscopic scale, vortex phenomena are observed, causing particles to converge within the cloud, resulting in collisions and aggregation. The vortices enhance the continuity of the cloud concentration. The magnitude of these vortices demonstrates a positive correlation with the magnitude of the semi-cone angle of the dispersal device. For a macroscopically stable cloud, the high-concentration area within the cloud moves outward radially with an increase in the semi-cone angle. This study provides a theoretical foundation for cloud morphology control technology, contributing to enhancing the safety and reliability of cloud systems.

DEM simulation of microscopic structure and macroscopic mechanical behavior of clay in oedometer and triaxial compression tests

An updated DEM simulation scheme for clay is implemented by incorporating convex polygon shaped platelets and robust algorithm for physico-chemical–mechanical interaction between clay platelets. Clays with two typical microscopic structures, i.e., card-house structure and book-house structure, are simulated in oedometer test and triaxial compression test. Virtual Mercury Intrusion Porosimetry (MIP) and pore segmentation algorithms are utilized to extract pore statistics from the numerical samples. The simulation results and experimental results from the literature are thoroughly compared at both macroscopic and microscopic scales. It is found that the implemented DEM simulation scheme can satisfactorily reproduce the experimentally observed macroscopic behavior of clay. Especially, in triaxial compression tests, the critical state behavior, stress-dilatancy relationship and over-consolidation effects are generally consistent with existing experimental results. General consistency in pore size, orientation and shape distributions in DEM simulations and experiments can be observed. Clay platelets tend to rotate to align their normal directions along the major principal stress direction, while pores tend to align their long-axis directions in the confinement direction, leading to dynamically evolving fabric anisotropy. In the book-house structure clay, clay domains experience multiple deformation modes, including uniaxial compaction, shear distortion, spatial spin, and their combinations.

Optimizing Resource Consumption and Reducing Power Usage in Data Centers, A Novel Mathematical VM Replacement Model and Efficient Algorithm

This study addresses the issue of power consumption in virtualized cloud data centers by proposing a virtual machine (VM) replacement model and a corresponding algorithm. The model incorporates multi-objective functions, aiming to optimize VM selection based on weights and minimize resource utilization disparities across hosts. Constraints are incorporated to ensure that CPU utilization remains close to the average CPU usage while mitigating overutilization in memory and network bandwidth usage. The proposed algorithm offers a fast and efficient solution with minimal VM replacements. The experimental simulation results demonstrate significant reductions in power consumption compared with a benchmark model. The proposed model and algorithm have been implemented and operated within a real-world cloud infrastructure, emphasizing their practicality.

A naïve Bayes regularized logistic regression estimator for low-dimensional classification

To reduce the estimator's variance and prevent overfitting, regularization techniques have attracted great interest from the statistics and machine learning communities. Most existing regularized methods rely on the sparsity assumption that a model with fewer parameters predicts better than one with many parameters. This assumption works particularly well in high-dimensional problems. However, the sparsity assumption may not be necessary when the number of predictors is relatively small compared to the number of training instances. This paper argues that shrinking the coefficients towards a low-variance data-driven estimate could be a better regularization strategy for such situations. For low-dimensional classification problems, we propose a naïve Bayes regularized logistic regression (NBRLR) that shrinks the logistic regression coefficients toward the naïve Bayes estimate to provide a reduction in variance. Our approach is primarily motivated by the fact that naïve Bayes is functionally equivalent to logistic regression if naïve Bayes' conditional independence assumption holds. Under standard conditions, we prove the consistency of the NBRLR estimator. Extensive simulation and empirical experimental results show that NBRLR is a competitive alternative to various state-of-the-art classifiers, especially on low-dimensional datasets.

Globally Guided Deep V-Network-Based Motion Planning Algorithm for Fixed-Wing Unmanned Aerial Vehicles.

Fixed-wing UAVs have shown great potential in both military and civilian applications. However, achieving safe and collision-free flight in complex obstacle environments is still a challenging problem. This paper proposed a hierarchical two-layer fixed-wing UAV motion planning algorithm based on a global planner and a local reinforcement learning (RL) planner in the presence of static obstacles and other UAVs. Considering the kinematic constraints, a global planner is designed to provide reference guidance for ego-UAV with respect to static obstacles. On this basis, a local RL planner is designed to accomplish kino-dynamic feasible and collision-free motion planning that incorporates dynamic obstacles within the sensing range. Finally, in the simulation training phase, a multi-stage, multi-scenario training strategy is adopted, and the simulation experimental results show that the performance of the proposed algorithm is significantly better than that of the baseline method.

A Steering-Vector-Based Matrix Information Geometry Method for Space–Time Adaptive Detection in Heterogeneous Environments

Plagued by heterogeneous clutter, it is a serious challenge for airborne radars to detect low-altitude, weak targets. To overcome this problem, a novel matrix information geometry detector for airborne multi-channel radar is proposed in this paper. The proposed detector applies the given steering vector and array structure information to the matrix information geometry detection method so that it can be used for space–time adaptive detection. While improving the detection performance, the matrix information geometry detector’s original anti-clutter advantage is enhanced as well. The simulation experiment results indicate that the proposed detector has advantages in several of the properties related to space–time adaptive detection, while its computational complexity does not increase significantly. Moreover, experiment results based on the measured data verify the superior performance of the proposed method. Sea-detecting data-sharing-program data, mountaintop data, and phased-array radar data are employed to verify the performance advantage of the proposed method in heterogeneous clutter and the ability for weak target detection.

Stability analysis of ultra-high and steep reinforced soil fills slopes based on strength reduction

The research focuses on solving the important problem of slope stability in the field of civil engineering. The study adopted an advanced strength double reduction coefficient method for slope stability analysis, which considers the different influence weights of cohesion and internal friction angle, and reduces them with different reduction coefficients to describe the stability of the slope. The simulation experiment results indicate that the attenuation degree of cohesion and internal friction angle affects slope stability. When the reduction coefficient of cohesion increases to 1.737 and the reduction coefficient of internal friction angle increases to 1.201, the slope is prone to instability and failure, and the safety factor of the calculated result is 1.493. Moreover, when the anti-slip pile is set in the middle of the slope (1/2), and the slope is in a critical state, the bending moment and shear force suffered by the anti-slip pile are both maximum, so the reinforcement effect is also the best.

An n-dimensional polynomial modulo chaotic map with controllable range of Lyapunov exponents and its application in color image encryption

In chaos-based image encryption, better performance of the chaotic system represents higher security of the encryption algorithm, and the Lyapunov exponent (LE) is an important measure to evaluate the chaotic performance. In this paper, an n-dimensional(n-D) polynomial modulo chaotic map (nD-PMCM) is proposed as a general model, whose range of Lyapunov exponent is controlled by the system parameters and the robust chaotic behavior with the desired complexity can be obtained. And an instantiated 2D-PMCM is constructed by setting the corresponding parameters in nD-PMCM, which exhibits superior chaotic performance compared to other chaotic maps. Then a novel color image encryption scheme based on the 2D-PMCM is also constructed, which mainly uses cross-plane cyclic shift permutation and cross-plane XOR diffusion. Different from the traditional encryption scheme with separate processing of color planes, the cross-plane operation disrupts the correlation between color image planes and has higher encryption efficiency due to the ability to disrupt the pixel position while also changing the pixel value. The experimental simulation results show that this encryption scheme has better security and encryption effects.

On the infiltration of cellular solids by sheet molding compound: process simulation and experimental validation

This study examines a numerical method to simulate the production of novel multi-material metal-composite components, where an additive-manufactured cellular solid is infiltrated by a sheet molding compound (SMC) in a single-step compression molding operation. A single-fiber numerical approach is adopted to predict microstructural changes, such as fiber orientation, fiber-matrix separation, and fiber volume content variations during molding. The accuracy of the numerical predictions is confirmed by physical samples using micro-computed tomography and optical microscopy investigations at both the qualitative and quantitative scales. From optical microscopy observations, there emerged a positive correlation between experimental outcomes and simulation results, accurately capturing fiber swirling, wrinkling, and draping that occurred during molding. At a quantitative scale, a 0.6% mismatch was observed when void volume and unfilled areas were compared, as measured by micro-computed tomography and numerical simulation.

An enhanced fireworks algorithm and its application in fault detection of the displacement sensor

Regarding the fault detection problem of the displacement sensor, an enhanced fireworks algorithm with information crossover and conversion factor (EFWA-IC) is proposed in this paper. In EFWA-IC, an information crossover strategy is proposed to maintain population diversity. This strategy is based on the predatory behavior of carnivores in nature. In addition, a conversion factor is set to control whether the explosion operator is executed or not to provide a more reasonable search. To fully evaluate the performance of EFWA-IC, a range of tests are carried out based on the CEC2017 and 23 classical test functions. The results show that the performance of EFWA-IC is better than other state-of-the-art optimization algorithms in terms of solution accuracy, convergence speed, and stability. Finally, EFWA-IC is utilized to optimize the particle filter (PF) to establish a fault detection model of displacement sensor in the continuous casting mold. The simulation experiment result of field data manifests that EFWA–IC–PF can accurately detect faults in the displacement sensor. For bias faults, the RMSE of the EFWA–IC–PF model is 0.14468, and the false alarm rate (FAR) and missed detection rate (MDR) are 1 % and 0.5 %, respectively. For stuck faults, the RMSE is 1.00148, and the FAR and MDR are 0.88 % and 1 %, respectively.