Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sle...Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing accidents.This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle.Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is achieved.One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement agencies.The suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.展开更多
In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only de...In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.展开更多
Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken en...Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken ends and output volt Ⅴ, when Ⅰ ≥60 mm, Ⅴ keeps constantly, the running speed v of wire-core belt has no big effect on output volt Ⅴ, there is inverse proportion between the height h from probe to the surface of the belt and output volt Ⅴ, when h≥30 mm, Ⅴ tends to be zero. Based on the test result, on-line monitoring installation is developed, the practice proved that the accuracy of broken wire monitoring can be above 95%, the monitoring accuracy of joint twitch can be 0 .04 Ⅴ/mm.展开更多
Energetic electron measurements and spacecraft charging are of great significance for theoretical research in space physics and space weather applications.In this paper,the energetic electron detection package(EEDP)de...Energetic electron measurements and spacecraft charging are of great significance for theoretical research in space physics and space weather applications.In this paper,the energetic electron detection package(EEDP)deployed on three Chinese navigation satellites in medium Earth orbit(MEO)is reviewed.The instrument was developed by the space science payload team led by Peking University.The EEDP includes a pinhole medium-energy electron spectrometer(MES),a high-energy electron detector(HED)based onΔE-E telescope technology,and a deep dielectric charging monitor(DDCM).The MES measures the energy spectra of 50−600 keV electrons from nine directions with a 180°×30°field of view(FOV).The HED measures the energy spectrum of 0.5−3.0 MeV electrons from one direction with a 30°cone-angle FOV.The ground test and calibration results indicate that these three sensors exhibit excellent performance.Preliminary observations show that the electron spectra measured by the MES and HED are in good agreement with the results from the magnetic electron-ion spectrometer(MagEIS)of the Van Allen Probes spacecraft,with an average relative deviation of 27.3%for the energy spectra.The charging currents and voltages measured by the DDCM during storms are consistent with the highenergy electron observations of the HED,demonstrating the effectiveness of the DDCM.The observations of the EEDP on board the three MEO satellites can provide important support for theoretical research on the radiation belts and the applications related to space weather.展开更多
针对煤炭运输过程中,经常无法保持煤炭在带式输送机上的运量均匀,使得带式输送机长时间全速运转而造成电能浪费和设备无效磨损的问题,提出一种基于语义分割的带式输送机煤料运输区域检测算法。该算法在DeeplabV3+的基础上,根据特征通道...针对煤炭运输过程中,经常无法保持煤炭在带式输送机上的运量均匀,使得带式输送机长时间全速运转而造成电能浪费和设备无效磨损的问题,提出一种基于语义分割的带式输送机煤料运输区域检测算法。该算法在DeeplabV3+的基础上,根据特征通道之间的相互依赖关系,引入注意力机制,使用不同扩张率的卷积核获得多种尺度的语义信息,来精确分割出煤炭在带式输送机的运输区域。实验结果表明,该算法平均交并比(Mean Intersection over Union,MIoU)相比于DeeplabV3+算法提高1.24百分点,能够有效精准地分割出煤料的运输区域,为煤量估计工作提供有效的保障。展开更多
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through Project Number MoE-IF-UJ-22-4100409-1.
文摘Nowadays days,the chief grounds of automobile accidents are driver fatigue and distractions.With the development of computer vision technology,a cutting-edge system has the potential to spot driver distractions or sleepiness and alert them,reducing accidents.This paper presents a novel approach to detecting driver tiredness based on eye and mouth movements and object identification that causes a distraction while operating a motor vehicle.Employing the facial landmarks that the camera picks up and sends to classify using a Convolutional Neural Network(CNN)any changes by focusing on the eyes and mouth zone,precision is achieved.One of the tasks that must be performed in the transit system is seat belt detection to lessen accidents caused by sudden stops or high-speed collisions with other cars.A method is put forth to use convolution neural networks to determine whether the motorist is wearing a seat belt when a driver is sleepy,preoccupied,or not wearing their seat belt,this system alerts them with an alarm,and if they don’t wake up by a predetermined time of 3 s threshold,an automatic message is sent to law enforcement agencies.The suggested CNN-based model exhibits greater accuracy with 97%.It can be utilized to develop a system that detects driver attention or sleeps in real-time.
基金This work was supported by the Shanxi Province Applied Basic Research Project,China(Grant No.201901D111100).Xiaoli Hao received the grant,and the URL of the sponsors’website is http://kjt.shanxi.gov.cn/.
文摘In underground mining,the belt is a critical component,as its state directly affects the safe and stable operation of the conveyor.Most of the existing non-contact detection methods based on machine vision can only detect a single type of damage and they require pre-processing operations.This tends to cause a large amount of calculation and low detection precision.To solve these problems,in the work described in this paper a belt tear detection method based on a multi-class conditional deep convolutional generative adversarial network(CDCGAN)was designed.In the traditional DCGAN,the image generated by the generator has a certain degree of randomness.Here,a small number of labeled belt images are taken as conditions and added them to the generator and discriminator,so the generator can generate images with the characteristics of belt damage under the aforementioned conditions.Moreover,because the discriminator cannot identify multiple types of damage,the multi-class softmax function is used as the output function of the discriminator to output a vector of class probabilities,and it can accurately classify cracks,scratches,and tears.To avoid the features learned incompletely,skiplayer connection is adopted in the generator and discriminator.This not only can minimize the loss of features,but also improves the convergence speed.Compared with other algorithms,experimental results show that the loss value of the generator and discriminator is the least.Moreover,its convergence speed is faster,and the mean average precision of the proposed algorithm is up to 96.2%,which is at least 6%higher than that of other algorithms.
文摘Electromagnetic self-induction theory and computer are adopted and study of online monitoring technique for wire-core belt is conducted, the study shows that there is direct proportion between distance Ⅰ of broken ends and output volt Ⅴ, when Ⅰ ≥60 mm, Ⅴ keeps constantly, the running speed v of wire-core belt has no big effect on output volt Ⅴ, there is inverse proportion between the height h from probe to the surface of the belt and output volt Ⅴ, when h≥30 mm, Ⅴ tends to be zero. Based on the test result, on-line monitoring installation is developed, the practice proved that the accuracy of broken wire monitoring can be above 95%, the monitoring accuracy of joint twitch can be 0 .04 Ⅴ/mm.
基金supported by the National Natural Science Foundation of China(No.41374167,41421003,41474140)China's National Basic Research and Development Program(No.2012CB825603).
文摘Energetic electron measurements and spacecraft charging are of great significance for theoretical research in space physics and space weather applications.In this paper,the energetic electron detection package(EEDP)deployed on three Chinese navigation satellites in medium Earth orbit(MEO)is reviewed.The instrument was developed by the space science payload team led by Peking University.The EEDP includes a pinhole medium-energy electron spectrometer(MES),a high-energy electron detector(HED)based onΔE-E telescope technology,and a deep dielectric charging monitor(DDCM).The MES measures the energy spectra of 50−600 keV electrons from nine directions with a 180°×30°field of view(FOV).The HED measures the energy spectrum of 0.5−3.0 MeV electrons from one direction with a 30°cone-angle FOV.The ground test and calibration results indicate that these three sensors exhibit excellent performance.Preliminary observations show that the electron spectra measured by the MES and HED are in good agreement with the results from the magnetic electron-ion spectrometer(MagEIS)of the Van Allen Probes spacecraft,with an average relative deviation of 27.3%for the energy spectra.The charging currents and voltages measured by the DDCM during storms are consistent with the highenergy electron observations of the HED,demonstrating the effectiveness of the DDCM.The observations of the EEDP on board the three MEO satellites can provide important support for theoretical research on the radiation belts and the applications related to space weather.
文摘针对煤炭运输过程中,经常无法保持煤炭在带式输送机上的运量均匀,使得带式输送机长时间全速运转而造成电能浪费和设备无效磨损的问题,提出一种基于语义分割的带式输送机煤料运输区域检测算法。该算法在DeeplabV3+的基础上,根据特征通道之间的相互依赖关系,引入注意力机制,使用不同扩张率的卷积核获得多种尺度的语义信息,来精确分割出煤炭在带式输送机的运输区域。实验结果表明,该算法平均交并比(Mean Intersection over Union,MIoU)相比于DeeplabV3+算法提高1.24百分点,能够有效精准地分割出煤料的运输区域,为煤量估计工作提供有效的保障。
