Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm

Frontiers in Bioengineering and Biotechnology, Journal Year: 2022, Volume and Issue: 10

Published: July 1, 2022

Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they often not smooth enough planning the path, number of turns was large, resulting high energy consumption. Aiming at unsmooth path problem four-wheel intelligent vehicle algorithm, this article proposed an improved genetic ant colony hybrid physical model established. This first optimization algorithm about heuristic function with adaptive change evaporation factor. Then, it on fitness function, adjustment crossover factor, mutation Last, addition a deletion operator, adoption elite retention strategy, suboptimal solutions obtained from to obtain optimized new populations. The simulation environment for is windows 10, processor Intel Core i5-5257U, running memory 4GB, compilation MATLAB2018b, samples 50, maximum iterations 100, initial population size 200, 50. Simulation experiments show that effective. Compared traditional reduced by 46% average 75% simple grid. 47% 21% complex works better reduce maps.

Language: Английский

---

Grasping Pose Detection for Loose Stacked Object Based on Convolutional Neural Network With Multiple Self-Powered Sensors Information

IEEE Sensors Journal, Journal Year: 2022, Volume and Issue: 23(18), P. 20619 - 20632

Published: Aug. 5, 2022

There are a variety of objects, random postures and multiple objects stacked in disorganized manner unstructured home applications, which leads to the object grasping posture estimation planning based on machine vision become very complicated. This paper proposes method cluttering pose detection convolutional neural network with self-powered sensors information. Firstly, search strategy for candidate poses 3D point cloud is proposed, single-channel image dataset representing this established by using Bigbird dataset. Secondly, ResNet constructed rank filter single channel captured images bit pose. It also compared three mainstream classification networks, Inception V2, VGG-A LetNet, perception analysis function execution developed under ROS. The effective manipulator scene scattered piles realized results position combined information sensors, other networks. In environment experiment show that superior average success rate ResNet, InceptionV2, VGGA LetNet networks 90.67%, 82.67%, 86.67% 87.33% respectively, verifies effectiveness superiority deep learn-based model proposed paper.

Language: Английский

---

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC

Frontiers in Bioengineering and Biotechnology, Journal Year: 2022, Volume and Issue: 10

Published: Feb. 28, 2022

Autonomous Underwater Vehicle are widely used in industries, such as marine resource exploitation and fish farming, but they often subject to a large amount of interference which cause poor control stability, while performing their tasks. A decoupling algorithm is proposed single volume-single attitude angle model constructed for the problem severe coupling system six degrees freedom Vehicle. Aiming at complex Active Disturbance Rejection Control (ADRC) adjustment relying on manual experience, PSO-ADRC realize automatic its parameters, improves anti-interference ability accuracy dynamic environment. The method were verified through experiments.

Language: Английский

---

Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition

Frontiers in Bioengineering and Biotechnology, Journal Year: 2022, Volume and Issue: 10

Published: June 7, 2022

As a key technology for the non-invasive human-machine interface that has received much attention in industry and academia, surface EMG (sEMG) signals display great potential advantages field of collaboration. Currently, gesture recognition based on sEMG suffers from inadequate feature extraction, difficulty distinguishing similar gestures, low accuracy multi-gesture recognition. To solve these problems new network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which signals. The multi-stream formed by embedding GRU module CBAM. Fusing ACC further improves action experimental results show proposed method obtains excellent performance dataset collected this paper with accuracies 94.1%, achieving advanced 89.7% Ninapro DB1 dataset. system high classifying 52 kinds different delay less than 300 ms, showing terms real-time human-computer interaction flexibility manipulator control.

Language: Английский

---

Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network

Frontiers in Bioengineering and Biotechnology, Journal Year: 2022, Volume and Issue: 10

Published: March 21, 2022

Recent work has shown that deep convolutional neural network is capable of solving inverse problems in computational imaging, and recovering the stress field loaded object from photoelastic fringe pattern can also be regarded as an problem process. However, formation affected by geometry specimen experimental configuration. When produces complex distribution, traditional analysis methods still face difficulty unwrapping. In this study, a based on encoder-decoder structure proposed, which accurately decode distribution information images generated under different configurations. The proposed method validated synthetic dataset, quality model evaluated using mean squared error (MSE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), other evaluation indexes. results show recovery achieve average performance more than 0.99 SSIM.

Language: Английский

---

Research Progress of Human–Computer Interaction Technology Based on Gesture Recognition

Electronics, Journal Year: 2023, Volume and Issue: 12(13), P. 2805 - 2805

Published: June 25, 2023

Gesture recognition, as a core technology of human–computer interaction, has broad application prospects and brings new technical possibilities for smart homes, medical care, sports training, other fields. Compared with the traditional interaction models based on PC use keyboards mice, gesture recognition-based modes can transmit information more naturally, flexibly, intuitively, which become research hotspot in field recent years. This paper described current status recognition technology, summarized principles development history electromagnetic wave sensor stress electromyographic visual improvement this by researchers years through direction structure, selection characteristic signals, algorithm signal processing, etc. By sorting out comparing typical cases four implementations, advantages disadvantages each implementation scenarios were discussed from two aspects dataset size accuracy. Based abovementioned discussion, problems challenges terms biocompatibility structures, wearability adaptability, stability, robustness, crossover acquisition analysis algorithms, future directions proposed.

Language: Английский

---

Biomimetic Vision for Zoom Object Detection Based on Improved Vertical Grid Number YOLO Algorithm

Frontiers in Bioengineering and Biotechnology, Journal Year: 2022, Volume and Issue: 10

Published: May 20, 2022

With the development of bionic computer vision for images processing, researchers have easily obtained high-resolution zoom sensing images. The drones equipped with high-definition cameras has greatly increased sample size and image segmentation target detection are important links during process information. As biomimetic remote usually prone to blur distortion in imaging, transmission processing stages, this paper improves vertical grid number YOLO algorithm. Firstly, light shade a were abstracted, grey-level cooccurrence matrix extracted feature parameters quantitatively describe texture characteristics image. Simple Linear Iterative Clustering (SLIC) superpixel method was used achieve light/dark scenes, saliency area obtained. Secondly, model segmenting dark scenes established made dataset meet recognition standard. Due refraction passing through lens other factors, difference contour boundary value between pixel background would make it difficult detect target, pixels main part separated be sharper edge detection. Thirdly, algorithm an improved proposed real time on processed array. adjusted aspect ratio modified grids network structure by using 20 convolutional layers five maximum aggregation layers, which more accurately adapted "short coarse" identified object information density. Finally, comparison mainstream algorithms different environments, test results aid showed that high spatial resolution images, higher accuracy than had real-time performance accuracy.

Language: Английский

---

Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

IET Image Processing, Journal Year: 2022, Volume and Issue: 17(4), P. 1280 - 1290

Published: Dec. 23, 2022

Abstract With the rapid development of sensor technology and artificial intelligence, video gesture recognition under background big data makes human‐computer interaction more natural flexible, bringing richer interactive experience to teaching, on‐board control, electronic games, etc. In order perform robust conditions illumination change, clutter, movement, partial occlusion, an algorithm based on multi‐level feature fusion two‐stream convolutional neural network is proposed, which includes three main steps. Firstly, Kinect obtains RGB‐D images establish a database. At same time, enhancement performed training test sets. Then, model established trained. Experiments result show that proposed can robustly track recognize gestures, compared with single‐channel model, average detection accuracy improved by 1.08%, mean precision (mAP) 3.56%. The rate gestures occlusion different light intensity was 93.98%. Finally, in ASL dataset, LaRED 1‐miohand shows satisfactory performances other method.

Language: Английский

---

Natural gas pipeline leak diagnosis based on improved variational modal decomposition and locally linear embedding feature extraction method

Process Safety and Environmental Protection, Journal Year: 2022, Volume and Issue: 164, P. 857 - 867

Published: May 21, 2022

Language: Английский

---

A Multimodal Multilevel Converged Attention Network for Hand Gesture Recognition With Hybrid sEMG and A-Mode Ultrasound Sensing

IEEE Transactions on Cybernetics, Journal Year: 2022, Volume and Issue: 53(12), P. 7723 - 7734

Published: Sept. 23, 2022

Gesture recognition based on surface electromyography (sEMG) has been widely used in the field of human–machine interaction (HMI). However, sEMG limitations, such as low signal-to-noise ratio and insensitivity to fine finger movements, so we consider adding A-mode ultrasound (AUS) enhance impact. To explore influence multisource sensing data gesture better integrate features different modules. We proposed a multimodal multilevel converged attention network (MMCANet) model for signals composed AUS. The extracts hidden AUS signal with convolutional neural (CNN). Meanwhile, CNN-LSTM (long-short memory network) hybrid structure some spatial-temporal from signal. Then, two types CNN are spliced transmitted transformer encoder fuse information interact produce features. Finally, classification results output employing fully connected layers. Attention mechanisms adjust weights feature channels. compared MMCANet's extraction performance that manually extracted sEMG-AUS using four traditional machine-learning (ML) algorithms. accuracy increased by at least 5.15%. In addition, tried deep learning (DL) methods single modals. experimental showed improved 14.31% 3.80% over method AUS, respectively. Compared state-of-the-art fusion techniques, our also achieved results.

Language: Английский

---