Intelligent Recognition Using Ultralight Multifunctional Nano‑Layered Carbon Aerogel Sensors with Human‑Like Tactile Perception

Humans can perceive our complex world through multi-sensory fusion.Under limited visual conditions,people can sense a variety of tactile signals to identify objects accurately and rapidly.However,replicating this unique capability in robots remains a significant challenge.Here,we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure,temperature,material recognition and 3D location capabilities,which is combined with multimodal supervised learning algorithms for object recognition.The sensor exhibits human-like pressure(0.04–100 kPa)and temperature(21.5–66.2℃)detection,millisecond response times(11 ms),a pressure sensitivity of 92.22 kPa^(−1)and triboelectric durability of over 6000 cycles.The devised algorithm has universality and can accommodate a range of application scenarios.The tactile system can identify common foods in a kitchen scene with 94.63%accuracy and explore the topographic and geomorphic features of a Mars scene with 100%accuracy.This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing,recognition and intelligence.

Structural plane recognition from three-dimensional laser scanning points using an improved region-growing algorithm based on the robust randomized Hough transform

The staggered distribution of joints and fissures in space constitutes the weak part of any rock mass.The identification of rock mass structural planes and the extraction of characteristic parameters are the basis of rock-mass integrity evaluation,which is very important for analysis of slope stability.The laser scanning technique can be used to acquire the coordinate information pertaining to each point of the structural plane,but large amount of point cloud data,uneven density distribution,and noise point interference make the identification efficiency and accuracy of different types of structural planes limited by point cloud data analysis technology.A new point cloud identification and segmentation algorithm for rock mass structural surfaces is proposed.Based on the distribution states of the original point cloud in different neighborhoods in space,the point clouds are characterized by multi-dimensional eigenvalues and calculated by the robust randomized Hough transform(RRHT).The normal vector difference and the final eigenvalue are proposed for characteristic distinction,and the identification of rock mass structural surfaces is completed through regional growth,which strengthens the difference expression of point clouds.In addition,nearest Voxel downsampling is also introduced in the RRHT calculation,which further reduces the number of sources of neighborhood noises,thereby improving the accuracy and stability of the calculation.The advantages of the method have been verified by laboratory models.The results showed that the proposed method can better achieve the segmentation and statistics of structural planes with interfaces and sharp boundaries.The method works well in the identification of joints,fissures,and other structural planes on Mangshezhai slope in the Three Gorges Reservoir area,China.It can provide a stable and effective technique for the identification and segmentation of rock mass structural planes,which is beneficial in engineering practice.

A System of Image Recognition-Based Railway Foreign Object Intrusion Monitoring Design

The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is made.Secondly,according to the functional demands,the system selects a suitable neural web,and the programming is reasonable.At last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).

AI-Driven FBMC-OQAM Signal Recognition via Transform Channel Convolution Strategy

With the advent of the Industry 5.0 era,the Internet of Things(IoT)devices face unprecedented proliferation,requiring higher communications rates and lower transmission delays.Considering its high spectrum efficiency,the promising filter bank multicarrier(FBMC)technique using offset quadrature amplitude modulation(OQAM)has been applied to Beyond 5G(B5G)industry IoT networks.However,due to the broadcasting nature of wireless channels,the FBMC-OQAMindustry IoT network is inevitably vulnerable to adversary attacks frommalicious IoT nodes.The FBMC-OQAMindustry cognitive radio network(ICRNet)is proposed to ensure security at the physical layer to tackle the above challenge.As a pivotal step of ICRNet,blind modulation recognition(BMR)can detect and recognize the modulation type of malicious signals.The previous works need to accomplish the BMR task of FBMC-OQAM signals in ICRNet nodes.A novel FBMC BMR algorithm is proposed with the transform channel convolution network(TCCNet)rather than a complicated two-dimensional convolution.Firstly,this is achieved by designing a low-complexity binary constellation diagram(BCD)gridding matrix as the input of TCCNet.Then,a transform channel convolution strategy is developed to convert the image-like BCD matrix into a serieslike data format,accelerating the BMR process while keeping discriminative features.Monte Carlo experimental results demonstrate that the proposed TCCNet obtains a performance gain of 8%and 40%over the traditional inphase/quadrature(I/Q)-based and constellation diagram(CD)-based methods at a signal noise ratio(SNR)of 12 dB,respectively.Moreover,the proposed TCCNet can achieve around 29.682 and 2.356 times faster than existing CD-Alex Network(CD-AlexNet)and I/Q-Convolutional Long Deep Neural Network(I/Q-CLDNN)algorithms,respectively.

Improving the Segmentation of Arabic Handwriting Using Ligature Detection Technique

Recognizing handwritten characters remains a critical and formidable challenge within the realm of computervision. Although considerable strides have been made in enhancing English handwritten character recognitionthrough various techniques, deciphering Arabic handwritten characters is particularly intricate. This complexityarises from the diverse array of writing styles among individuals, coupled with the various shapes that a singlecharacter can take when positioned differently within document images, rendering the task more perplexing. Inthis study, a novel segmentation method for Arabic handwritten scripts is suggested. This work aims to locatethe local minima of the vertical and diagonal word image densities to precisely identify the segmentation pointsbetween the cursive letters. The proposed method starts with pre-processing the word image without affectingits main features, then calculates the directions pixel density of the word image by scanning it vertically and fromangles 30° to 90° to count the pixel density fromall directions and address the problem of overlapping letters, whichis a commonly attitude in writing Arabic texts by many people. Local minima and thresholds are also determinedto identify the ideal segmentation area. The proposed technique is tested on samples obtained fromtwo datasets: Aself-curated image dataset and the IFN/ENIT dataset. The results demonstrate that the proposed method achievesa significant improvement in the proportions of cursive segmentation of 92.96% on our dataset, as well as 89.37%on the IFN/ENIT dataset.

Automatic infrared image recognition method for substation equipment based on a deep self-attention network and multi-factor similarity calculation

Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.

Synergistic piezoelectricity enhanced BaTiO_(3)/polyacrylonitrile elastomer-based highly sensitive pressure sensor for intelligent sensing and posture recognition applications

Designing stretchable and skin-conformal self-powered sensors for intelligent sensing and posture recognition is challenging.Here,based on a multi-force mixing and vulcanization process,as well as synergistically piezoelectricity of BaTiO_(3)and polyacrylonitrile,an all-in-one,stretchable,and self-powered elastomer-based piezo-pressure sensor(ASPS)with high sensitivity is reported.The ASPS presents excellent sensitivity(0.93 V/104 Pa of voltage and 4.92 nA/104 Pa of current at a pressure of 10-200 kPa)and high durability(over 10,000 cycles).Moreover,the ASPS exhibits a wide measurement range,good linearity,rapid response time,and stable frequency response.All components were fabricated using silicone,affording satisfactory skinconformality for sensing postures.Through cooperation with a homemade circuit and artificial intelligence algorithm,an information processing strategy was proposed to realize intelligent sensing and recognition.The home-made circuit achieves the acquisition and wireless transmission of ASPS signals(transmission distance up to 50 m),and the algorithm realizes the classification and identification of ASPS signals(accuracy up to 99.5%).This study proposes not only a novel fabrication method for developing self-powered sensors,but also a new information processing strategy for intelligent sensing and recognition,which offers significant application potential in human-machine interaction,physiological analysis,and medical research.

Seismic description and fluid identification of thin reservoirs in Shengli Chengdao extra-shallow sea oilfield

The meandering channel deposit of the upper member of Neogene Guantao Formation in Shengli Chengdao extra-shallow sea oilfield is characterized by rapid change in sedimentary facies.In addition,affected by surface tides and sea water reverberation,the double sensor seismic data processed by conventional methods has low signal-to-noise ratio and low resolution,and thus cannot meet the needs of seismic description and oil-bearing fluid identification of thin reservoirs less than 10 meters thick in this area.The two-step high resolution frequency bandwidth expanding processing technology was used to improve the signal-to-noise ratio and resolution of the seismic data,as a result,the dominant frequency of the seismic data was enhanced from 30 Hz to 50 Hz,and the sand body thickness resolution was enhanced from 10 m to 6 m.On the basis of fine layer control by seismic data,three types of seismic facies models,floodplain,natural levee and point bar,were defined,and the intelligent horizon-facies controlled recognition technology was worked out,which had a prediction error of reservoir thickness of less than 1.5 m.Clearly,the description accuracy of meandering channel sand bodies has been improved.The probability semi-quantitative oiliness identification method of fluid by prestack multi-parameters has been worked out by integrating Poisson’s ratio,fluid factor,product of Lame parameter and density,and other prestack elastic parameters,and the method has a coincidence rate of fluid identification of more than 90%,providing solid technical support for the exploration and development of thin reservoirs in Shengli Chengdao extra-shallow sea oilfield,which is expected to provide reference for the exploration and development of similar oilfields in China.

Forest Fire Smoke Detection Method Based on MoAm-YOLOv4 Algorithm

To improve the performance of the forest fire smoke detection model and achieve a better balance between detection accuracy and speed, an improved YOLOv4 detection model (MoAm-YOLOv4) that combines a lightweight network and attention mechanism was proposed. Based on the YOLOv4 algorithm, the backbone network CSPDarknet53 was replaced with a lightweight network MobilenetV1 to reduce the model’s size. An attention mechanism was added to the three channels before the output to increase its ability to extract forest fire smoke effectively. The algorithm used the K-means clustering algorithm to cluster the smoke dataset, and obtained candidate frames that were close to the smoke images;the dataset was expanded to 2000 images by the random flip expansion method to avoid overfitting in training. The experimental results show that the improved YOLOv4 algorithm has excellent detection effect. Its mAP can reach 93.45%, precision can get 93.28%, and the model size is only 45.58 MB. Compared with YOLOv4 algorithm, MoAm-YOLOv4 improves the accuracy by 1.3% and reduces the model size by 80% while sacrificing only 0.27% mAP, showing reasonable practicability.

Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision

The harvesting time of fresh tea leaves has a significant impact on product yield and quality.The aim of this study was to propose a method for real-time monitoring of the optimum harvesting time for picking fresh tea leaves based on machine vision.Firstly,the shapes of fresh tea leaves were distinguished from RGB images of the tea-tree canopy after graying with the improved B-G algorithm,filtering with a median filter algorithm,binary processing with the Otsu algorithm,and noise reduction and edge smoothing using open and close operations.Then the leaf characteristics,such as leaf area index,average length,and leaf identification index,were calculated.Based on these,the Bayesian discriminant principle and method were used to construct a discriminant model for fresh tea-leaf collection status.When this method was applied to a RGB tea-tree canopy image acquired at 45°shooting angle,the fresh tea-leaf recognition rate was 90.3%,and the accuracy for fresh tea-leaf harvesting status was 98%by cross validation.Hence,this method provides the basic conditions for future tea-plantation operation and management using information technology,automation,and intelligent systems.