Modulation Algorithm Research Articles

Long, Fibrous, and Tailorable Dielectric Elastomer Artificial Muscles via Mask‐Free Stamping of Carbon Nanotube Electrodes

AbstractHigh‐performance, large‐size artificial muscles are in great demand in the field of robotics. This work reports a comprehensive approach for fabricating, actuating, sensing, and controlling a 180 mm‐long fibrous dielectric elastomer (DE) artificial muscle. The fabrication process introduces a novel method for patterning large‐area, uniform electrodes using vacuum filtration followed by mask‐free stamping on DE substrates. To address the challenge of long charging times that impair dynamic performance, an amplitude modulation algorithm is developed for high‐frequency actuation, which increased generated strain by 64% at a resonant frequency of 10 Hz. Additionally, the hollow space within the rolled artificial muscle is used to integrate a waveguide that serves as a strain sensor. This combined actuation‐sensing structure maintains flexibility and actuation capabilities while enabling self‐sensing and feedback control. The versatility of the DE artificial muscle is further demonstrated by segmenting a single long muscle into three shorter units and employing these units to construct two multi‐actuator machines: a tensegrity‐based gimbal and a rotary engine. This work advances the large‐scale production and application of DE artificial muscles.

Light Field Modulation Algorithms for Spatial Light Modulators: A Review

The coding method of spatial light modulator is the core key of spatial light field modulation technology, and the needs of the modulation algorithm are different under the specified mode and application requirements. This paper first reviews the progress made in recent years in light field control algorithms for digital micromirror devices (DMDs) and liquid crystal spatial light modulators (LC-SLM). Based on existing algorithms, the impact of optimization methods is analyzed. Then, the application areas of the different algorithms are summarized, and the characteristics of the control algorithms are analyzed. In addition, this review highlights innovative breakthroughs achieved by using various coding schemes and spatial light modulators (SLM) to manipulate the light field. Finally, critical future challenges facing emerging control algorithm technologies are outlined, while prospects for developing SLM control algorithms are proposed.

Implementation of PSK modulation and demodulation algorithms

Implementation of PSK modulation and demodulation algorithms

Transmission system with adaptive LDPC-Coded OFDM modulation implemented in GNU Radio

Real-time testing in wireless channel conditions of Forward Error Correction (FEC) coding techniques, Modulation and Coding Scheme (MCS) selection algorithms is a complex and challenging task. GNU Radio and Software Defined Radio (SDR) concept allow a wide range of practical and low-cost solutions to perform the testing operations mentioned above. The paper proposes a duplex transmission system with adaptive LDPC (Low Density Parity Check) coding and OFDM (Orthogonal Frequency Division Multiplexing) modulation implemented in GNU Radio. The frame structure, design principles, system architecture, and mechanisms controlling the transmission process are presented. The system’s modular architecture provides the framework for testing various LDPC coding and MCS selection techniques. The system’s design allows us to maximize the transmission’s efficiency, the encapsulation of various data structures, i.e., frame and transport block, using reduced overheads and the necessity of padding operations is minimized.

Experimental optimal control of servo-pneumatic with sliding mode and GA-fuzzy-PID-PWM

Due to the inherent nonlinearity of pneumatic systems, achieving accurate control of pneumatic actuators remains a challenging task. This study compares three control strategies-PID control, fuzzy control, and sliding mode control-used to guide the motion of pneumatic actuators driven by on/off solenoid valves. The primary objective is to evaluate and compare the performance of each controller in the context of servo-pneumatic systems, highlighting their respective advantages and disadvantages. On/off solenoid valves, which reduce system costs, are utilized in this study. Since these valves can only process on/off signals, pulse width modulation (PWM) is employed to modulate the input signal, adjusting the duty cycle and performance time of the valves. Initially, the governing equations for the actuator and valves are presented, followed by an introduction to each controller. The pulse width modulation algorithm is then discussed, which regulates the control signal to the valve’s duty cycle and performance time. Finally, simulation results are provided, comparing the controllers and illustrating their strengths and weaknesses.

Artificial Empathy and Imprecise Communication in a Multi-Agent System

This paper introduces a novel artificial intelligence model that integrates artificial empathy into the decision-making processes of collaborative agent systems. The existing models of collaborative behaviors, especially in swarm applications, lack the aspect of empathy, known to improve cooperation in human teams. Emphasizing both cognitive and emotional aspects of empathy, the introduced model navigates communication uncertainties and ambiguities, transforming these challenges into opportunities for learning and adaptation in dynamic environments. A significant feature of this model is its handling of imprecision through fuzzy logic, using fuzzy similarity measures in the decision process. The main objective of the presented research is to introduce a new model for improving cooperativeness in multi-agent systems with the use of cognitive empathy. Future research focus on implementing the model on physical platform and optimize the artificial empathy algorithms in the decision-making module.

A Secure Data Hiding For H.264 Video Based on Chaotic Map Methods and RC4 Algorithm

Video steganography is the common field of data security in digital communication where data include text, image, and video. Compressed video is the suitable modern source for hiding huge secret data that will trust security. This work proposed a compressed video steganography scheme for hiding cipher secret data in high security levels. The proposed work consists of three phases, protected secret data (Text English and Arabic, Images, and Video) based on proposed chaotic maps module and RC4 algorithm. The chaotic maps module is a combination between (honen map and sine map) with simple operations to protected plain data. Second phase, hidden cipher secret data between the quantized discrete cosine transform (QDCT) and entropy coding from the inter-prediction mode at the H.264/AVC video frames using Least Significant Bit (LSB) replacement. Third phase, transmit and receive secret data. The experimental result shows that the proposed scheme is high-security level where was cipher secret data with the average entropy value of (7.993), an average PSNR value of (8.790 dB), an average correlation value of (3.4x 10-3), key length is 228 bit and key space is 2228. Moreover, measured Randomness to generation key by NIST were succeeded in all the tests and Robustness, imperceptibility, and embedding capacity are high while maintaining visual quality, where the average PSNR value of (36.26 dB), and the average SSIM value of (0.95).

A hydraulic control system integrating adaptive and PWM algorithms for hydro-mechanical continuously variable transmission

IntroductionWith the development of technology, the hydraulic control system for hydro-mechanical continuously variable transmission has also made rapid progress. However, the traditional control system for hydro-mechanical continuously variable transmission has drawbacks such as efficiency loss and slow response speed. The study fused the adaptive and pulse width modulation algorithms to solve these problems effectively, and they were applied to the hydraulic mechanical continuously variable drive control system.MethodFirst, the study analyzed the continuously variable transmission control of hydraulic machinery and designed the control scheme on the basis of the analysis. Then, a new scheme fusing adaptive and pulse-width modulation algorithms was proposed on the basis of the control scheme. Finally, simulation experiments and practical tests were carried out to verify the performance of the control scheme.ResultsThe results showed that the time to reach the maximum pressure was 2.15 s when the volume of the pilot valve cavity was 5 × 109/L. When the oil flow rate was 70 L/min, the corresponding maximum and minimum values of the main oil circuit pressure were 1.63 MPa and 0.37 MPa, respectively. The time to reach the maximum value of the pressure was 2.05 s, respectively. These values were in line with the design requirements and were better than the comparison values.DiscussionThese results confirm that the system has important theoretical and practical significance, which has a positive promoting effect on the development of the hydraulic machinery industry. This article aims to provide a new approach for research and application in related fields.

Design of a Supercapacitor Module and Control Algorithm for Practical Verification of a Hybrid Energy Storage System

This paper presents an approach to designing a supercapacitor (SC) module according to defined power profiles and providing a control algorithm for sharing the energy from the SC module and accumulator in a hybrid energy storage system (HESS). This paper also presents a view of a printed circuit board (PCB) of the SC module and an interconnection board between the bidirectional converter, accumulator, and SC module. The practical part of the paper presents the measurement of the voltages and currents on the SC module, accumulator, and output of the DC/DC converter to visualize the energy flow between them.

Polish Speech and Text Emotion Recognition in a Multimodal Emotion Analysis System

Emotion recognition by social robots is a serious challenge because sometimes people also do not cope with it. It is important to use information about emotions from all possible sources: facial expression, speech, or reactions occurring in the body. Therefore, a multimodal emotion recognition system was introduced, which includes the indicated sources of information and deep learning algorithms for emotion recognition. An important part of this system includes the speech analysis module, which was decided to be divided into two tracks: speech and text. An additional condition is the target language of communication, Polish, for which the number of datasets and methods is very limited. The work shows that emotion recognition using a single source—text or speech—can lead to low accuracy of the recognized emotion. It was therefore decided to compare English and Polish datasets and the latest deep learning methods in speech emotion recognition using Mel spectrograms. The most accurate LSTM models were evaluated on the English set and the Polish nEMO set, demonstrating high efficiency of emotion recognition in the case of Polish data. The conducted research is a key element in the development of a decision-making algorithm for several emotion recognition modules in a multimodal system.

Adaptive Granularity-Fused Keypoint Detection for 6D Pose Estimation of Space Targets

Estimating the 6D pose of a space target is an intricate task due to factors such as occlusions, changes in visual appearance, and background clutter. Accurate pose determination requires robust algorithms capable of handling these complexities while maintaining reliability under various environmental conditions. Conventional pose estimation for space targets unfolds in two stages: establishing 2D–3D correspondences using keypoint detection networks and 3D models, followed by pose estimation via the perspective-n-point algorithm. The accuracy of this process hinges critically on the initial keypoint detection, which is currently limited by predominantly singular-scale detection techniques and fails to exploit sufficient information. To tackle the aforementioned challenges, we propose an adaptive dual-stream aggregation network (ADSAN), which enables the learning of finer local representations and the acquisition of abundant spatial and semantic information by merging features from both inter-layer and intra-layer perspectives through a multi-grained approach, consolidating features within individual layers and amplifying the interaction of distinct resolution features between layers. Furthermore, our ADSAN implements the selective keypoint focus module (SKFM) algorithm to alleviate problems caused by partial occlusions and viewpoint alterations. This mechanism places greater emphasis on the most challenging keypoints, ensuring the network prioritizes and optimizes its learning around these critical points. Benefiting from the finer and more robust information of space objects extracted by the ADSAN and SKFM, our method surpasses the SOTA method PoET (5.8°, 8.1°/0.0351%, 0.0744%) by 0.5°, 0.9°, and 0.0084%, 0.0354%, achieving 5.3°, 7.2° in rotation angle errors and 0.0267%, 0.0390% in normalized translation errors on the Speed and SwissCube datasets, respectively.

Improving quality of electricity generated by grid-tied inverters in solar power plants in low generated power mode using frequency adaptive PWM

Abstract The article presents the results of a study of the dependence of the quality indicators of electricity generated by solar power plants in three-phase electrical networks on the level of generated power. In the course of the study, it was established that when the generated power is reduced, the grid voltage inverters that are part of the solar power plants cause an increase in the share of higher harmonic currents to the electric grid. An analysis of international standards regarding the quality requirements of the generated current was performed, which showed that when the level of generated power decreases, the quality of electric energy decreases and may not meet the requirements of the standard. The algorithm of frequency-adaptive pulse-width modulation is proposed, which allows to improve the quality indicators of the electricity generated by the grid inverter of the voltage of the solar power plant to the electrical grid.

Adaptive frequency modulation optimization algorithm for supporting power grid of large-scale energy storage power station based on small samples

Abstract Addressing frequency instability issues stemming from scarce modulation resources and limited willingness to modulate in large-scale energy storage power station grids, this paper introduces a small-sample-based adaptive optimization algorithm for frequency modulation. Leveraging cross-correlation coefficients of small sample nodes, it analyzes and extracts frequency modulation characteristics, optimizing existing fluctuations to enhance adaptive control efficiency. Experimental outcomes demonstrate its effectiveness in adaptively optimizing frequency modulation, yielding low modulation and initial disturbances across varying scenarios, thereby showcasing robust adaptive optimization performance.

Defect intelligent recognition of membrane product based on deep learning

Defect detection plays a crucial role in the manufacturing industry, ensuring the quality of industrial products. Despite advancements in this field, current defect detection methods face two primary challenges: (1) extracting visually similar features from the background poses difficult and (2) these methods struggle to identify tiny defects in the target objects. We present a feature enhancement module called the SE-CAR, which aims to handle the identified problems effectively. This module is designed to efficiently capture tiny defects in images, prioritize defect information features, and ultimately enhance the model’s predictive performance and accuracy in defect recognition tasks. In addition, Distance-IoU Non-Maximum Suppression is employed as a substitution for the original Non-Maximum Suppression. This enhances the recognition accuracy of bounding boxes, ensuring that the model maintains high detection accuracy even after complex scenarios. Moreover, the proposed methodology exhibits broad applicability across a wide spectrum of prevalent defect detection paradigms. In empirical experiments, we employ the YOLOv7 architecture as the foundation framework, integrating the proposed methodologies for the purpose of detecting defects in the membrane. The empirical evidence demonstrates a notable improvement in the performance of detecting defects in membrane products using the SE-CAR feature enhancement module and Distance-IoU Non-Maximum Suppression algorithm. In comparison to baseline networks, an increase in mAp by 6.29%, precision by 1.63%, and recall by 8.76%.

CNTs@fabric/Ni@PU piezoresistive sensor with enhanced interfacial contact resistance variation for motion detection and deep-learning-assisted recognition

Abstract Flexible piezoresistive sensors based on the mechanism of interfacial contact resistance change are receiving increasing attention in the fields of human-computer interaction, health monitoring, and behavior tracking. However, the high cost and complex manufacturing process limit the wide application and development of these flexible piezoresistive sensors. Here, a novel carbon nanotubes@fabric (CNTs@fabric)/Ni@polyurethane (Ni@PU) piezoresistive sensor (CNPS) with low-cost, simple-preparation and high-sensitivity was proposed. The effective contact area is obtained by synergizing the woven micro-convex structure of the fabric, the large specific surface area of the CNTs and the porous three dimensional electrodes. Within the small pressure (0–9.52 kPa) effect, the area of connection with the electrodes to the active layer plays a dominant role, resulting in a sensitivity of up to 6.39 kPa−1 for CNPS. In the high pressure region (9.52–44.92 kPa), where internal mechanism of change in the sensitive material dominants, the CNPS has a response time of 85 ms at a constant pressure of 28.31 kPa. Considering the excellent output electrical performances, a variety of body movements could be detected by fixing the CNPS to different joints. Significantly, the designed intelligent object recognition system implemented by the combination of matrix stress detection module and residual neural network (ResNet) algorithm has a recognition accuracy of 99.26%. Enhancing the interfacial contact resistance change mechanism using a simple fabrication process offers a promising strategy for the rapid development of flexible piezoresistive sensors.

Development of the Real-time Algorithm for Intra-string Photovoltaic Modules Faults Detection and Classification

Development of the Real-time Algorithm for Intra-string Photovoltaic Modules Faults Detection and Classification

Short-Term Prediction of Origin–Destination Passenger Flow in Urban Rail Transit Systems with Multi-Source Data: A Deep Learning Method Fusing High-Dimensional Features

Short-term origin–destination (OD) passenger flow forecasting is crucial for urban rail transit enterprises aiming to optimise transportation products and increase operating income. As there are large-scale OD pairs in an urban rail transit system, OD passenger flow cannot be obtained in real time (temporal hysteresis). Additionally, the distribution characteristics are also complex. Previous studies mainly focus on passenger flow prediction at metro stations, while few methods solve the OD passenger flow prediction problems of an urban rail transit system. In view of this, we propose a novel deep learning method fusing high-dimensional features (HDF-DL) with multi-source data. The HDF-DL method is combined with three modules. The temporal module incorporates the time-varying, trend, and cyclic characteristics of OD passenger flow, while the latest OD passenger flow time sequence (within 1 h) is excluded from the time-varying characteristics. In the spatial module, the K-means and K-shape algorithms are used to classify OD pairs from multiple perspectives and capture the spatial features, reducing the difficulty of OD passenger flow predictions with large-scale and complex characteristics. Weather factors are considered in the external feature module. The HDF-DL method is tested on a large-scale metro system in China, in which eight baseline models are designed. The results show that the HDF-DL method achieves high prediction accuracy across multiple time granularities, with a mean absolute percentage error of about 10%. OD passenger flow in every departure time interval can be predicted with high and stable accuracy, effectively capturing temporal characteristics. The modular design of HDF-DL, which fuses high-dimensional features and employs appropriate neural networks for different data types, significantly reduces prediction errors and outperforms baseline models.

Evaluation of the effectiveness of incomplete phase pulse width modulation algorithms in the converter-electric motor system

Aim. This study evaluates the effectiveness of incomplete-phase algorithms for pulse width modulation of three-phase voltages in frequency control of electric drives, using integral current dispersion in the load as the criterion. Materials and methods. This study examines pulse width modulation processes in frequency-controlled electric drives, focusing on the converter-electric motor system. Methods from electric circuit theory are used, with proposed algorithms illustrated through drawings, and these drawing can be implemented in the matrix laboratory software. Results. The process of pulse width modulation in the converter-electric motor system is considered. It is demonstrated that to minimize the number of switching keys in the frequency converter, incomplete-phase pulse width modulation algorithms are beneficial. Expressions for local dispersion of the interphase current in single-phase and two-phase modulation are analyzed. An expression for local current dispersion in a three-phase bridge circuit load is derived, and the concept of integral current dispersion in a three-phase load is derived. Indicators for evaluating the effectiveness of algorithms for incomplete-phase pulse width modulation are proposed. Graphs the efficiency coefficient, characterizing incomplete-phase pulse width modulation with a minimum number of switching of key elements in the function of the amplitude coefficient and in the function of the relative frequency of modulation are constructed. Conclusion. The results aid in developing algorithms for controlling frequency converters in asynchronous electric drive systems.

A real time data-driven dynamic glasius bionic neural network path planning algorithm for polar under-ice feature scanning by “Xinghai 1000” AUV

Using Autonomous Underwater Vehicle (AUV) to survey polar under-ice feature is crucial for studying polar marine environment and global climate change. Compared with seafloor terrain scanning, it faces several challenges, such as uncertain under-ice environment, lack of reliable positioning methods, and stringent constraints on recovery location. Thus, taking “Xinghai 1000” as analysis object, this paper proposes a real time data-driven dynamic glasius bionic neural network path planning algorithm (RDGPP), which consists of an online bi-level path planning module. In global planning module, dynamically hierarchical strategy is used to realize task area layering. On this basis, global coverage path is planned by combining neuron activity value updated which utilize real-time environmental information obtained by FLS sonar with multivariant strategy. Moreover, due to AUV recovery mode limitation, return strategy has been devised in multivariant strategy to ensure AUV automatic return to launch point, thereby reducing energy usage. In local planning module, Flagged bit and GBNN-driven A-star algorithm (FGA) is introduced to minimize deadlock escape time for AUV. Simulation experiments verify RDGPP algorithm advancement in key indicators such as turning times, running time, etc., and effectiveness is verified by “Xinghai 1000” under-ice experiment.

Maritime infrared image enhancement based on morphological pseudotransmittance modulation and radiation source enhancement

To address the problems of low contrast and fuzzy details in maritime infrared images, this paper constructs a pseudotransmittance model based on the principles of infrared radiation imaging, and proposes an enhancement algorithm of morphology single-scale pseudotransmittance modulation and radiation source enhancement. First, the original image is adaptively segmented to generate the radiation source region and the radiation source suppressed background. Then, the pseudotransmittance is calculated by estimating the radiation of the background through morphological methods. Finally, the pseudotransmittance is used to modulate the original image for background enhancement and the radiation source detail is enhanced as well. On this basis, an algorithm for maritime infrared image enhancement with morphological multiscale pseudotransmittance modulation and radiation source hierarchical fusion is developed to overcome the problem of edge overenhancement and neighbourhood enhancement. Experiments show that our methods perform well in detail and contrast enhancement and outperform other methods in both qualitative and quantitative evaluations.